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ML Research Scientist, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
$120k–245k
406 days ago
♡
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
$140k–245k
406 days ago
♡
ML Research Scientist, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
$120k–245k
406 days ago
♡
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
$140k–245k
406 days ago
♡
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ML & Molecular Simulation Scientist
Genesis Molecular AI ·
Pay
$70k–145k
Setting
On-site
ML & Molecular Simulation Scientist
Location
San Mateo, CA
Employment Type
Full time
Department
AI
Overview
Application
About the Team
At Genesis Molecular AI, we're a tight-knit team of deep learning researchers, computational scientists, and drug discovery pioneers united by a single mission: to develop the next generation of AI-driven therapies for patients with severe diseases.
We don't just apply machine learning to biology – we conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
At Genesis, simulation and machine learning aren't separate disciplines: they're deeply integrated, and the scientists who do this work sit at the center of everything we build. You will work side by side with world-class researchers across ML, chemistry, and biology, with access to large-scale compute infrastructure and simulation pipelines, contributing to a platform where physics-based methods and AI advance together.
About the Role
We are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, and see those methods through to real impact in drug discovery programs.
This is a role for someone who thrives at the intersection of computational science and machine learning: designing and running simulations, building ML models grounded in physical intuition, and collaborating directly with CADD and discovery teams to move molecules from hit identification to lead optimization.
Some areas you may focus on:
Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property prediction
Integrate physics-based and ML + data-driven approaches, combining force field methods, quantum chemistry, and structure-based design with modern ML to improve accuracy and throughput
Develop and apply simulation methods spanning molecular dynamics, enhanced sampling (metadynamics, replica exchange, umbrella sampling), and free energy calculations (FEP/TI) to support active drug discovery programs
Contribute to the GEMS platform, improving our generative AI and scoring capabilities, focusing on 3D methods; strengthen ML and physics-based scoring functions (and their intersection), build next-gen force fields
Work directly with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis through lead optimization
Stay current with the field, implementing and adapting methods from the latest literature in geometric ML, biomolecular simulation, and computational drug design
Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists
Who You Are
Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures (e.g., SE(3)/E(3) networks), or diffusion models applied to molecular data
PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field; postdoctoral or industry experience is a plus
Deep, hands-on expertise in molecular simulation, including MD, enhanced sampling, and/or free energy methods using tools such as GROMACS, AMBER, OpenMM, or NAMD
Familiarity with structure-based drug design workflows: docking, binding site analysis, protein-ligand interaction modeling using tools such as MOE, or PyMOL
Proficiency in Python and scientific computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit); comfort with HPC environments and scripting for large-scale simulation workflows
A track record of applying computational methods to real scientific problems, demonstrated through publications, open-source contributions, or industry impact
Collaborative, curious, and able to move between rigorous method development and fast-paced discovery work
Nice to Have
Familiarity with cheminformatics and ADMET property prediction
Contributions to open-source simulation or ML tooling
What We Offer
Highly competitive compensation including base, bonus, and equity
Comprehensive health, dental, and vision insurance (fully covered for employees)
Stock option eligibility
401(k) plan
Open PTO policy
Paid company holidays
Daily meals and snacks in the office
Flexible work environment
About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML & Molecular Simulation Scientist
Genesis Molecular AI ·
Pay
$70k–145k
Setting
On-site
About the Team
At Genesis Molecular AI, we're a tight-knit team of deep learning researchers, computational scientists, and drug discovery pioneers united by a single mission: to develop the next generation of AI-driven therapies for patients with severe diseases.
We don't just apply machine learning to biology – we conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
At Genesis, simulation and machine learning aren't separate disciplines: they're deeply integrated, and the scientists who do this work sit at the center of everything we build. You will work side by side with world-class researchers across ML, chemistry, and biology, with access to large-scale compute infrastructure and simulation pipelines, contributing to a platform where physics-based methods and AI advance together.
About the Role
We are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, and see those methods through to real impact in drug discovery programs.
This is a role for someone who thrives at the intersection of computational science and machine learning: designing and running simulations, building ML models grounded in physical intuition, and collaborating directly with CADD and discovery teams to move molecules from hit identification to lead optimization.
Some areas you may focus on:
- Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property prediction
- Integrate physics-based and ML + data-driven approaches, combining force field methods, quantum chemistry, and structure-based design with modern ML to improve accuracy and throughput
- Develop and apply simulation methods spanning molecular dynamics, enhanced sampling (metadynamics, replica exchange, umbrella sampling), and free energy calculations (FEP/TI) to support active drug discovery programs
- Contribute to the GEMS platform, improving our generative AI and scoring capabilities, focusing on 3D methods; strengthen ML and physics-based scoring functions (and their intersection), build next-gen force fields
- Work directly with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis through lead optimization
- Stay current with the field, implementing and adapting methods from the latest literature in geometric ML, biomolecular simulation, and computational drug design
- Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists
Who You Are
- Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures (e.g., SE(3)/E(3) networks), or diffusion models applied to molecular data
- PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field; postdoctoral or industry experience is a plus
- Deep, hands-on expertise in molecular simulation, including MD, enhanced sampling, and/or free energy methods using tools such as GROMACS, AMBER, OpenMM, or NAMD
- Familiarity with structure-based drug design workflows: docking, binding site analysis, protein-ligand interaction modeling using tools such as MOE, or PyMOL
- Proficiency in Python and scientific computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit); comfort with HPC environments and scripting for large-scale simulation workflows
- A track record of applying computational methods to real scientific problems, demonstrated through publications, open-source contributions, or industry impact
- Collaborative, curious, and able to move between rigorous method development and fast-paced discovery work
Nice to Have
- Familiarity with cheminformatics and ADMET property prediction
- Contributions to open-source simulation or ML tooling
What We Offer
- Highly competitive compensation including base, bonus, and equity
- Comprehensive health, dental, and vision insurance (fully covered for employees)
- Stock option eligibility
- 401(k) plan
- Open PTO policy
- Paid company holidays
- Daily meals and snacks in the office
- Flexible work environment
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML & Molecular Simulation Scientist
Genesis Molecular AI ·
Pay
$70k–145k
Setting
On-site
About the Team
At Genesis Molecular AI, we're a tight-knit team of deep learning researchers, computational scientists, and drug discovery pioneers united by a single mission: to develop the next generation of AI-driven therapies for patients with severe diseases.
We don't just apply machine learning to biology – we conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
At Genesis, simulation and machine learning aren't separate disciplines: they're deeply integrated, and the scientists who do this work sit at the center of everything we build. You will work side by side with world-class researchers across ML, chemistry, and biology, with access to large-scale compute infrastructure and simulation pipelines, contributing to a platform where physics-based methods and AI advance together.
About the Role
We are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, and see those methods through to real impact in drug discovery programs.
This is a role for someone who thrives at the intersection of computational science and machine learning: designing and running simulations, building ML models grounded in physical intuition, and collaborating directly with CADD and discovery teams to move molecules from hit identification to lead optimization.
Some areas you may focus on:
- Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property prediction
- Integrate physics-based and ML + data-driven approaches, combining force field methods, quantum chemistry, and structure-based design with modern ML to improve accuracy and throughput
- Develop and apply simulation methods spanning molecular dynamics, enhanced sampling (metadynamics, replica exchange, umbrella sampling), and free energy calculations (FEP/TI) to support active drug discovery programs
- Contribute to the GEMS platform, improving our generative AI and scoring capabilities, focusing on 3D methods; strengthen ML and physics-based scoring functions (and their intersection), build next-gen force fields
- Work directly with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis through lead optimization
- Stay current with the field, implementing and adapting methods from the latest literature in geometric ML, biomolecular simulation, and computational drug design
- Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists
Who You Are
- Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures (e.g., SE(3)/E(3) networks), or diffusion models applied to molecular data
- PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field; postdoctoral or industry experience is a plus
- Deep, hands-on expertise in molecular simulation, including MD, enhanced sampling, and/or free energy methods using tools such as GROMACS, AMBER, OpenMM, or NAMD
- Familiarity with structure-based drug design workflows: docking, binding site analysis, protein-ligand interaction modeling using tools such as MOE, or PyMOL
- Proficiency in Python and scientific computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit); comfort with HPC environments and scripting for large-scale simulation workflows
- A track record of applying computational methods to real scientific problems, demonstrated through publications, open-source contributions, or industry impact
- Collaborative, curious, and able to move between rigorous method development and fast-paced discovery work
Nice to Have
- Familiarity with cheminformatics and ADMET property prediction
- Contributions to open-source simulation or ML tooling
What We Offer
- Highly competitive compensation including base, bonus, and equity
- Comprehensive health, dental, and vision insurance (fully covered for employees)
- Stock option eligibility
- 401(k) plan
- Open PTO policy
- Paid company holidays
- Daily meals and snacks in the office
- Flexible work environment
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Scientist, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$120k–245k
Setting
Remote
ML Research Scientist, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry.
At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative and discriminative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.
About the Role
This is an opportunity for a scientific innovator to advance the future of generative AI in drug discovery. As a key member of the Genesis AI team, you will shape and drive our research agenda for foundation models. You will lead critical research initiatives in areas like reinforcement learning, novel model architectures, and advanced pretraining and post-training methods. Your core mission is to create groundbreaking models and insights that are instrumental in discovering new medicines.
This role requires a deep curiosity and a collaborative spirit. You will be a strong team player, working in close partnership with our exceptional engineers and drug discovery experts to bring complex ideas to life. We also want you to be a voice in the scientific community. We will actively support and champion your efforts to publish some research breakthroughs in premier ML venues such as NeurIPS, ICML, and ICLR.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You will
Lead transformative research projects from conception to implementation, tackling core challenges in generative modeling for molecular systems.
Design and develop novel models and algorithms, building upon the latest literature in diffusion models, flow matching, RL, LLMs, and other cutting-edge areas.
Execute ambitious experiments at scale, leveraging our world-class computational infrastructure to rigorously validate your hypotheses and push the boundaries of the state-of-the-art.
Collaborate intensely with a multidisciplinary team to translate your research into tangible impact on our drug discovery platform.
Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
Mentor and guide other researchers and engineers, fostering a culture of shared learning, growth, and innovation.
You are
A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary – only willingness to learn.
A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
Nice to have's
PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
Experience in distributed training and inference of large models on GPU clusters.
Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.
Compensation, Benefits, and Perks
Competitive compensation package that includes salary and equity.
Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
401(k) plan.
Open (unlimited) PTO policy.
Free lunches and dinners at our offices.
Paid family leave (maternity and paternity).
Life and long- and short-term disability insurance.
About Genesis Molecular AI
is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$140k–245k
Setting
Hybrid
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Location
San Mateo, CA; New York, NY
Employment Type
Full time
Location Type
Hybrid
Department
AI
Overview
Application
ML Research Engineer, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers.
Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases. We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
The Genesis AI team is building the engine for this revolution. We develop large-scale generative models trained across the full spectrum of molecular data, supported by extensive compute infrastructure and simulation pipelines.
The work sits at the intersection of machine learning research, structural biology, and computational chemistry, requiring deep technical rigor and strong interdisciplinary collaboration.
About the Role
This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering.
As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science.
You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs.
Your work may involve:
Scaling model pretraining pipelines
Advancing reinforcement learning or post-training systems
Optimizing performance of large molecular models
Bringing structure prediction models like Pearl into production environments used by chemists and drug programs
This role requires someone who can bridge ML and computational chemistry, translating between disciplines and helping teams move quickly from research insight to deployed capability.
We are looking for someone who can own problems end-to-end, in a fast-moving research environment, translating novel ML ideas into systems that scientists can use in active discovery programs.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You Will
Drive the R&D and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
Make cutting-edge foundation model research a reality at scale. Implement, optimize, and build novel foundation models from the initial research prototypes to high-performance production models.
Optimize performance of large-scale ML systems, including distributed training, inference efficiency, and GPU-level optimizations where necessary.
Constantly engage with deep learning literature, building upon novel architectures and training methods to create new capabilities.
Bridge machine learning research and computational chemistry workflows, working closely with computational chemists, structural biologists, and medicinal chemists to ensure models translate effectively into real drug discovery programs.
Help productionize Pearl and related structure prediction models, enabling reliable deployment and integration into Genesis’ internal and partner drug discovery pipelines.
Own the experimental lifecycle with scientific rigor. You'll design experimental plans, own their execution on our large-scale compute infrastructure, and drive the deep analysis of results to inform the next research cycle and to validate most promising approaches.
Ship state-of-the-art models to production,
Collaborate intensely. Work closely with the broader team to integrate your models into our drug discovery platform.
Mentor and guide other researchers and engineers, fostering a culture of high-quality code, rigorous experimentation, and continuous innovation.
Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
Who You Are
2+ years industry experience of building complex ML systems.
A research engineer with deep ML rigor.
You have deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them.
A builder who ships.
You write clean, high-performance code and are comfortable working across the ML stack (Python, PyTorch, distributed training systems). You have demonstrated experience translating research into working systems quickly.
An expert in modern ML engineering.
You understand the mathematics and systems behind modern ML methods. You can design, optimize, and implement novel modeling approaches.
Experienced in training models at scale.
You understand distributed training, large-scale datasets, and performance optimization across GPU clusters. You thrive in environments where models move rapidly from prototype to production.
Experience with GPU systems programming
Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks.
Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
Comfortable in research ambiguity.
You can iterate on novel architectures, training pipelines, and experimental ideas while maintaining rigorous engineering discipline.
A first-principles thinker.
You approach problems from fundamentals and take pride in building robust systems from conceptual design to state-of-the-art implementation.
A curious mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
A strong cross-functional collaborator.
You communicate effectively with scientists across disciplines including computational chemistry, structural biology, and medicinal chemistry.
No prior biology or chemistry experience is required, though curiosity and willingness to learn are essential.
Nice to haves
Experience with novel research in one or more of the following domains: LLMs, diffusion, reinforcement learning or other cutting edge generative or predictive machine learning models.
Computational chemistry or drug discovery systems
Especially experience related to protein-ligand structure prediction, small-molecule modeling, or computational drug discovery workflows.
Generative modeling methods
Diffusion models or other generative architectures applied to scientific or molecular problems.
LLM post-training techniques
Experience with SFT, RLHF, synthetic data pipelines, or other post-training systems.
Performance engineering
Experience with Triton kernels, TensorRT, quantization, or large-scale model serving.
Publications in top-tier ML venues
NeurIPS, ICML, ICLR, or similar.
Experience with ML frameworks used at Genesis
PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, or related systems.
Advanced degree
MS or PhD in machine learning, computer science, computational science, or equivalent research/engineering experience.
What we offer
Competitive compensation package that includes salary and equity.
Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
401(k) plan.
Open (unlimited) PTO policy.
Free lunches and dinners at our offices.
Paid family leave (maternity and paternity).
Life and long- and short-term disability insurance.
About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Scientist, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$120k–245k
Setting
Remote
ML Research Scientist, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry.
At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative and discriminative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.
About the Role
This is an opportunity for a scientific innovator to advance the future of generative AI in drug discovery. As a key member of the Genesis AI team, you will shape and drive our research agenda for foundation models. You will lead critical research initiatives in areas like reinforcement learning, novel model architectures, and advanced pretraining and post-training methods. Your core mission is to create groundbreaking models and insights that are instrumental in discovering new medicines.
This role requires a deep curiosity and a collaborative spirit. You will be a strong team player, working in close partnership with our exceptional engineers and drug discovery experts to bring complex ideas to life. We also want you to be a voice in the scientific community. We will actively support and champion your efforts to publish some research breakthroughs in premier ML venues such as NeurIPS, ICML, and ICLR.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You will
- Lead transformative research projects from conception to implementation, tackling core challenges in generative modeling for molecular systems.
- Design and develop novel models and algorithms, building upon the latest literature in diffusion models, flow matching, RL, LLMs, and other cutting-edge areas.
- Execute ambitious experiments at scale, leveraging our world-class computational infrastructure to rigorously validate your hypotheses and push the boundaries of the state-of-the-art.
- Collaborate intensely with a multidisciplinary team to translate your research into tangible impact on our drug discovery platform.
- Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
- Mentor and guide other researchers and engineers, fostering a culture of shared learning, growth, and innovation.
You are
- A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
- An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
- A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
- A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary – only willingness to learn.
- A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
- Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
Nice to have's
- PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
- Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
- Experience in distributed training and inference of large models on GPU clusters.
- Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.
Compensation, Benefits, and Perks
- Competitive compensation package that includes salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at our offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$140k–245k
Setting
Hybrid
ML Research Engineer, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers.
Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases. We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
The Genesis AI team is building the engine for this revolution. We develop large-scale generative models trained across the full spectrum of molecular data, supported by extensive compute infrastructure and simulation pipelines.
The work sits at the intersection of machine learning research, structural biology, and computational chemistry, requiring deep technical rigor and strong interdisciplinary collaboration.
About the Role
This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering.
As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science.
You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs.
Your work may involve:
- Scaling model pretraining pipelines
- Advancing reinforcement learning or post-training systems
- Optimizing performance of large molecular models
- Bringing structure prediction models like Pearl into production environments used by chemists and drug programs
This role requires someone who can bridge ML and computational chemistry, translating between disciplines and helping teams move quickly from research insight to deployed capability.
We are looking for someone who can own problems end-to-end, in a fast-moving research environment, translating novel ML ideas into systems that scientists can use in active discovery programs.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You Will
- Drive the R&D and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
- Make cutting-edge foundation model research a reality at scale. Implement, optimize, and build novel foundation models from the initial research prototypes to high-performance production models.
- Optimize performance of large-scale ML systems, including distributed training, inference efficiency, and GPU-level optimizations where necessary.
- Constantly engage with deep learning literature, building upon novel architectures and training methods to create new capabilities.
- Bridge machine learning research and computational chemistry workflows, working closely with computational chemists, structural biologists, and medicinal chemists to ensure models translate effectively into real drug discovery programs.
- Help productionize Pearl and related structure prediction models, enabling reliable deployment and integration into Genesis’ internal and partner drug discovery pipelines.
- Own the experimental lifecycle with scientific rigor. You'll design experimental plans, own their execution on our large-scale compute infrastructure, and drive the deep analysis of results to inform the next research cycle and to validate most promising approaches.
- Ship state-of-the-art models to production,
- Collaborate intensely. Work closely with the broader team to integrate your models into our drug discovery platform.
- Mentor and guide other researchers and engineers, fostering a culture of high-quality code, rigorous experimentation, and continuous innovation.
- Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
Who You Are
- 2+ years industry experience of building complex ML systems.
- A research engineer with deep ML rigor.
You have deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them.
- A builder who ships.
You write clean, high-performance code and are comfortable working across the ML stack (Python, PyTorch, distributed training systems). You have demonstrated experience translating research into working systems quickly.
- An expert in modern ML engineering.
You understand the mathematics and systems behind modern ML methods. You can design, optimize, and implement novel modeling approaches.
- Experienced in training models at scale.
You understand distributed training, large-scale datasets, and performance optimization across GPU clusters. You thrive in environments where models move rapidly from prototype to production.
- Experience with GPU systems programming
Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks.
- Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
- Comfortable in research ambiguity.
You can iterate on novel architectures, training pipelines, and experimental ideas while maintaining rigorous engineering discipline.
- A first-principles thinker.
You approach problems from fundamentals and take pride in building robust systems from conceptual design to state-of-the-art implementation.
- A curious mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
- A strong cross-functional collaborator.
You communicate effectively with scientists across disciplines including computational chemistry, structural biology, and medicinal chemistry.
- No prior biology or chemistry experience is required, though curiosity and willingness to learn are essential.
Nice to haves
- Experience with novel research in one or more of the following domains: LLMs, diffusion, reinforcement learning or other cutting edge generative or predictive machine learning models.
- Computational chemistry or drug discovery systems
Especially experience related to protein-ligand structure prediction, small-molecule modeling, or computational drug discovery workflows.
- Generative modeling methods
Diffusion models or other generative architectures applied to scientific or molecular problems.
- LLM post-training techniques
Experience with SFT, RLHF, synthetic data pipelines, or other post-training systems.
- Performance engineering
Experience with Triton kernels, TensorRT, quantization, or large-scale model serving.
- Publications in top-tier ML venues
NeurIPS, ICML, ICLR, or similar.
- Experience with ML frameworks used at Genesis
PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, or related systems.
- Advanced degree
MS or PhD in machine learning, computer science, computational science, or equivalent research/engineering experience.
What we offer
- Competitive compensation package that includes salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at our offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Scientist, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$120k–245k
Setting
Remote
ML Research Scientist, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry.
At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
We don’t just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative and discriminative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.
About the Role
This is an opportunity for a scientific innovator to advance the future of generative AI in drug discovery. As a key member of the Genesis AI team, you will shape and drive our research agenda for foundation models. You will lead critical research initiatives in areas like reinforcement learning, novel model architectures, and advanced pretraining and post-training methods. Your core mission is to create groundbreaking models and insights that are instrumental in discovering new medicines.
This role requires a deep curiosity and a collaborative spirit. You will be a strong team player, working in close partnership with our exceptional engineers and drug discovery experts to bring complex ideas to life. We also want you to be a voice in the scientific community. We will actively support and champion your efforts to publish some research breakthroughs in premier ML venues such as NeurIPS, ICML, and ICLR.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You will
- Lead transformative research projects from conception to implementation, tackling core challenges in generative modeling for molecular systems.
- Design and develop novel models and algorithms, building upon the latest literature in diffusion models, flow matching, RL, LLMs, and other cutting-edge areas.
- Execute ambitious experiments at scale, leveraging our world-class computational infrastructure to rigorously validate your hypotheses and push the boundaries of the state-of-the-art.
- Collaborate intensely with a multidisciplinary team to translate your research into tangible impact on our drug discovery platform.
- Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
- Mentor and guide other researchers and engineers, fostering a culture of shared learning, growth, and innovation.
You are
- A deep learning expert with a portfolio of novel research in one or more cutting-edge domains of generative or predictive modeling (e.g., diffusion, flow matching, RL, foundation model architectures and training methods).
- An independent, first-principles thinker with a strong sense of ownership over your research projects and a drive to see them through to completion.
- A strong coder, comfortable with going deep into the engineering stack to build, debug, and ship your own models.
- A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary – only willingness to learn.
- A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
- Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
Nice to have's
- PhD in machine learning, computer science, other computational sciences or equivalent research experience, demonstrated by a strong publication record.
- Hands-on experience with Pytorch, Pytorch Lightning, Ray Distributed Training, Pytorch Geometric, etc.
- Experience in distributed training and inference of large models on GPU clusters.
- Familiarity with molecular data, (proteins, small molecules), physics-informed ML, or 3D point cloud data.
Compensation, Benefits, and Perks
- Competitive compensation package that includes salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at our offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Research Engineer, Foundation Models (Senior / Staff / Principal)
Genesis Molecular AI ·
Pay
$140k–245k
Setting
Hybrid
ML Research Engineer, Foundation Models
About the Team
Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers.
Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases. We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.
The Genesis AI team is building the engine for this revolution. We develop large-scale generative models trained across the full spectrum of molecular data, supported by extensive compute infrastructure and simulation pipelines.
The work sits at the intersection of machine learning research, structural biology, and computational chemistry, requiring deep technical rigor and strong interdisciplinary collaboration.
About the Role
This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering.
As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science.
You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs.
Your work may involve:
- Scaling model pretraining pipelines
- Advancing reinforcement learning or post-training systems
- Optimizing performance of large molecular models
- Bringing structure prediction models like Pearl into production environments used by chemists and drug programs
This role requires someone who can bridge ML and computational chemistry, translating between disciplines and helping teams move quickly from research insight to deployed capability.
We are looking for someone who can own problems end-to-end, in a fast-moving research environment, translating novel ML ideas into systems that scientists can use in active discovery programs.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
You Will
- Drive the R&D and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
- Make cutting-edge foundation model research a reality at scale. Implement, optimize, and build novel foundation models from the initial research prototypes to high-performance production models.
- Optimize performance of large-scale ML systems, including distributed training, inference efficiency, and GPU-level optimizations where necessary.
- Constantly engage with deep learning literature, building upon novel architectures and training methods to create new capabilities.
- Bridge machine learning research and computational chemistry workflows, working closely with computational chemists, structural biologists, and medicinal chemists to ensure models translate effectively into real drug discovery programs.
- Help productionize Pearl and related structure prediction models, enabling reliable deployment and integration into Genesis’ internal and partner drug discovery pipelines.
- Own the experimental lifecycle with scientific rigor. You'll design experimental plans, own their execution on our large-scale compute infrastructure, and drive the deep analysis of results to inform the next research cycle and to validate most promising approaches.
- Ship state-of-the-art models to production,
- Collaborate intensely. Work closely with the broader team to integrate your models into our drug discovery platform.
- Mentor and guide other researchers and engineers, fostering a culture of high-quality code, rigorous experimentation, and continuous innovation.
- Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
Who You Are
- 2+ years industry experience of building complex ML systems.
- A research engineer with deep ML rigor.
You have deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them.
- A builder who ships.
You write clean, high-performance code and are comfortable working across the ML stack (Python, PyTorch, distributed training systems). You have demonstrated experience translating research into working systems quickly.
- An expert in modern ML engineering.
You understand the mathematics and systems behind modern ML methods. You can design, optimize, and implement novel modeling approaches.
- Experienced in training models at scale.
You understand distributed training, large-scale datasets, and performance optimization across GPU clusters. You thrive in environments where models move rapidly from prototype to production.
- Experience with GPU systems programming
Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks.
- Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
- Comfortable in research ambiguity.
You can iterate on novel architectures, training pipelines, and experimental ideas while maintaining rigorous engineering discipline.
- A first-principles thinker.
You approach problems from fundamentals and take pride in building robust systems from conceptual design to state-of-the-art implementation.
- A curious mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
- A strong cross-functional collaborator.
You communicate effectively with scientists across disciplines including computational chemistry, structural biology, and medicinal chemistry.
- No prior biology or chemistry experience is required, though curiosity and willingness to learn are essential.
Nice to haves
- Experience with novel research in one or more of the following domains: LLMs, diffusion, reinforcement learning or other cutting edge generative or predictive machine learning models.
- Computational chemistry or drug discovery systems
Especially experience related to protein-ligand structure prediction, small-molecule modeling, or computational drug discovery workflows.
- Generative modeling methods
Diffusion models or other generative architectures applied to scientific or molecular problems.
- LLM post-training techniques
Experience with SFT, RLHF, synthetic data pipelines, or other post-training systems.
- Performance engineering
Experience with Triton kernels, TensorRT, quantization, or large-scale model serving.
- Publications in top-tier ML venues
NeurIPS, ICML, ICLR, or similar.
- Experience with ML frameworks used at Genesis
PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, or related systems.
- Advanced degree
MS or PhD in machine learning, computer science, computational science, or equivalent research/engineering experience.
What we offer
- Competitive compensation package that includes salary and equity.
- Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
- 401(k) plan.
- Open (unlimited) PTO policy.
- Free lunches and dinners at our offices.
- Paid family leave (maternity and paternity).
- Life and long- and short-term disability insurance.
About Genesis Molecular AI
Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl https://www.businesswire.com/news/home/20251028030745/en/Genesis-Molecular-AI-Unveils-Pearl-a-Field-Leading-Foundation-Model-that-Achieves-Unprecedented-Performance-in-Drug-Protein-Structure-Prediction for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes https://www.forbes.com/sites/innovationrx/2026/05/20/inside-incytes-120-million-ai-for-drug-development-deal/ and GEN https://www.genengnews.com/topics/artificial-intelligence/small-molecules-to-big-partnership-incyte-genesis-expand-ai-collaboration-to-1b/) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.
Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
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