Machine Learning Infrastructure Engineer
Location
San Mateo, CA; New York, NY; Remote
Employment Type
Full time
Location Type
Hybrid
Department
AI
Overview
Application
About the Team
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.
You will work alongside machine learning researchers, computational scientists, and engineers to build the infrastructure that transforms massive, heterogeneous protein and chemical datasets into reliable, high-performance inputs for our models and drug discovery workflows.
About the Role
We are looking for a Machine Learning Infrastructure Engineer to build the data and orchestration systems that power molecular AI at Genesis. Drug discovery creates unusual infrastructure challenges: our workflows combine computational chemistry, structural biology, machine learning, and large-scale data processing in ways that don't map neatly onto traditional ETL systems. To meet these requirements, we have built our own pipeline orchestration framework—and we want an engineer excited to push it much further.
This role is deliberately split between building the platform and using it. Roughly 50% of your time will be spent designing and improving our in-house orchestration framework: its DAG abstractions, execution engine, scheduling, caching, observability, and developer experience. The other 50% will be spent building and optimizing the protein, chemical, and ML data preprocessing pipelines that run on top of it. You'll work across both layers, finding opportunities to eliminate unnecessary computation, reduce latency, introduce lazy evaluation and caching, and make complex scientific workflows fast, reproducible, and easy for researchers to use.
Your day-to-day work will span:
Design and evolve our in-house workflow orchestration framework, including DAG construction and execution, dependency management, scheduling, caching, retries, observability, and distributed execution.
Build and optimize large-scale preprocessing pipelines for protein structures, chemical datasets, simulations, and machine learning training data.
Profile end-to-end workflows and aggressively eliminate bottlenecks—from redundant I/O and serialization to unnecessary recomputation and poorly parallelized workloads.
Develop abstractions for lazy execution, incremental computation, intelligent caching, and artifact reuse so expensive scientific computations happen only when necessary.
Partner closely with ML researchers, computational chemists, and scientific software engineers to translate complex research workflows into scalable, reproducible computational pipelines.
Improve the developer experience for scientists and engineers authoring pipelines, making sophisticated distributed workflows intuitive to define, debug, monitor, and extend.
Make architectural decisions spanning local and distributed execution, storage, compute scheduling, data lineage, and reproducibility.
Use lessons from real-world scientific pipelines to continuously improve the orchestration platform itself—and use improvements to the platform to unlock faster, more ambitious scientific workflows.
You are
Passionate about DAGs. You naturally think about computation as graphs of dependencies and care deeply about how work is scheduled, parallelized, cached, retried, and recomputed.
Impatient about latency. When a pipeline takes hours, your instinct is to understand exactly where the time went and systematically make it faster.
An evangelist for lazy execution and caching. You dislike unnecessary work and look for principled ways to avoid recomputation, move less data, and reuse intermediate results.
A strong systems engineer. You are comfortable reasoning across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance.
Hands-on with data-intensive systems. You have built production pipelines or infrastructure that processes large datasets reliably and efficiently.
Comfortable moving between framework and application layers. You're as interested in designing the orchestration primitive as you are in optimizing the pipeline built with it.
A pragmatic abstraction builder. You can identify the common pattern hiding underneath many specialized workflows without forcing every scientific problem into an overly generic framework.
A strong collaborator with researchers. You can understand an evolving scientific workflow, identify its computational structure, and turn it into robust infrastructure without slowing down experimentation.
Energized by unusual problems. You enjoy environments where off-the-shelf infrastructure gets you 80% of the way there—and the interesting work is designing the remaining 20%.
Nice to haves
Experience building or operating large-scale ETL, data processing, or workflow systems such as Apache Spark, GCP Dataflow / Apache Beam, Flyte, Dagster, Airflow, Ray, or similar infrastructure.
Experience designing workflow engines, schedulers, DAG execution systems, build systems, or other dependency-driven computation frameworks.
Experience optimizing large-scale scientific, ML, protein, cheminformatics, or computational biology data pipelines.
Familiarity with Kubernetes and containerized compute environments, including deploying and operating distributed workloads across heterogeneous CPU and GPU resources.
Familiarity with Terraform or similar infrastructure-as-code tooling for provisioning and managing the underlying cloud, storage, networking, and compute resources that support large-scale pipelines.
Experience with cloud object storage, distributed compute environments, and high-performance or GPU-based computing.
Experience with content-addressable storage, incremental computation, data lineage, memoization, or cache invalidation at scale.
Familiarity with molecular data formats and tools such as RDKit, OpenEye, BioPython, molecular dynamics tooling, or structural biology pipelines.
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 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.
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Listed by Genesis Molecular AI for a position based in the United States. Employers on this board attest they are hiring domestically.
Applied ML Scientist (Staff / Principal)
Location
San Mateo, CA; New York, NY; Remote
Employment Type
Full time
Location Type
Hybrid
Department
AI
Overview
Application
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. You will work side-by-side with top multidisciplinary researchers to design and build generative foundation models at scale, having access to ample compute and large-scale simulations.
About the Role
This unique role is for a scientist who is passionate about being a catalyst for applying cutting-edge AI to solve real-world drug discovery challenges. You will be the critical bridge between our long-term research and our experimental drug discovery programs. Your mission is to build, evaluate, monitor, and improve our state-of-the-art models directly into active drug programs, leading the charge on model validation, deployment, and analysis to guide the discovery of new medicines.
You will act as both a translator and a strategist, ensuring our research is aimed at the most critical challenges and that our drug hunters can leverage the full power of our industry-leading AI platform. This role requires a deep understanding of cheminformatics, computational chemistry, and experimental techniques, strong data science skills, and a talent for communicating complex ideas to a diverse, multidisciplinary team.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
What You’ll Do
Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality.
Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty.
Evaluate model quality by validating predictions against project data and internal or external benchmarks.
Curate internal and external datasets for model training and validation (in collaboration with experimental teams).
Contribute to design and analysis of experiments on model changes and alternative architectures.
You are
A seasoned computational scientist with a proven track record of machine learning based methods to impact small molecule drug discovery projects.
A cheminformatics expert, fluent in the language of molecular data with hands-on mastery of tools like RDKit or OpenEye.
A scientist who speaks the language of experimental drug discovery, with a strong familiarity with common assay types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.).
A rigorous data scientist, with experience inmodeling and analysis of small molecule datasets and passion for statistical validation, uncertainty quantification, and deriving clear insights from complex, noisy data.
A hands-on applied scientist and software engineer with strong coding skills in Python and a deep practical knowledge of the applied ML toolkit (e.g., scikit-learn, PyTorch).
An exceptional communicator and collaborator, able to act as the bridge between machine learning researchers and experimental scientists.
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.
A true team player 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
A PhD in Cheminformatics, Computational Chemistry, Computer Science, or a related field.A track record of publications applying machine learning to drug discovery challenges.
Deep expertise in advanced modeling techniques such as graph neural networks, multitask modeling, active learning, or Bayesian optimization.
Experience with large-scale data management, including SQL databases and data pipelining tools.
Strong opinions on molecule featurization and model validation.
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 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.
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. You will work side-by-side with top multidisciplinary researchers to design and build generative foundation models at scale, having access to ample compute and large-scale simulations.
About the Role
This unique role is for a scientist who is passionate about being a catalyst for applying cutting-edge AI to solve real-world drug discovery challenges. You will be the critical bridge between our long-term research and our experimental drug discovery programs. Your mission is to build, evaluate, monitor, and improve our state-of-the-art models directly into active drug programs, leading the charge on model validation, deployment, and analysis to guide the discovery of new medicines.
You will act as both a translator and a strategist, ensuring our research is aimed at the most critical challenges and that our drug hunters can leverage the full power of our industry-leading AI platform. This role requires a deep understanding of cheminformatics, computational chemistry, and experimental techniques, strong data science skills, and a talent for communicating complex ideas to a diverse, multidisciplinary team.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
What You’ll Do
- Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality.
- Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty.
- Evaluate model quality by validating predictions against project data and internal or external benchmarks.
- Curate internal and external datasets for model training and validation (in collaboration with experimental teams).
- Contribute to design and analysis of experiments on model changes and alternative architectures.
You are
- A seasoned computational scientist with a proven track record of machine learning based methods to impact small molecule drug discovery projects.
- A cheminformatics expert, fluent in the language of molecular data with hands-on mastery of tools like RDKit or OpenEye.
- A scientist who speaks the language of experimental drug discovery, with a strong familiarity with common assay types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.).
- A rigorous data scientist, with experience inmodeling and analysis of small molecule datasets and passion for statistical validation, uncertainty quantification, and deriving clear insights from complex, noisy data.
- A hands-on applied scientist and software engineer with strong coding skills in Python and a deep practical knowledge of the applied ML toolkit (e.g., scikit-learn, PyTorch).
- An exceptional communicator and collaborator, able to act as the bridge between machine learning researchers and experimental scientists.
- 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.
- A true team player 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
- A PhD in Cheminformatics, Computational Chemistry, Computer Science, or a related field.A track record of publications applying machine learning to drug discovery challenges.
- Deep expertise in advanced modeling techniques such as graph neural networks, multitask modeling, active learning, or Bayesian optimization.
- Experience with large-scale data management, including SQL databases and data pipelining tools.
- Strong opinions on molecule featurization and model validation.
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.
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. You will work side-by-side with top multidisciplinary researchers to design and build generative foundation models at scale, having access to ample compute and large-scale simulations.
About the Role
This unique role is for a scientist who is passionate about being a catalyst for applying cutting-edge AI to solve real-world drug discovery challenges. You will be the critical bridge between our long-term research and our experimental drug discovery programs. Your mission is to build, evaluate, monitor, and improve our state-of-the-art models directly into active drug programs, leading the charge on model validation, deployment, and analysis to guide the discovery of new medicines.
You will act as both a translator and a strategist, ensuring our research is aimed at the most critical challenges and that our drug hunters can leverage the full power of our industry-leading AI platform. This role requires a deep understanding of cheminformatics, computational chemistry, and experimental techniques, strong data science skills, and a talent for communicating complex ideas to a diverse, multidisciplinary team.
Positions are available at various levels of seniority: Senior, Staff, and Principal.
What You’ll Do
- Work directly with project teams to assess model performance and utility, including applicability to current project needs, and collaborate with ML and engineering teams to resolve issues or add new functionality.
- Assist experimental colleagues with use and interpretation of model predictions by providing context about model quality and prediction uncertainty.
- Evaluate model quality by validating predictions against project data and internal or external benchmarks.
- Curate internal and external datasets for model training and validation (in collaboration with experimental teams).
- Contribute to design and analysis of experiments on model changes and alternative architectures.
You are
- A seasoned computational scientist with a proven track record of machine learning based methods to impact small molecule drug discovery projects.
- A cheminformatics expert, fluent in the language of molecular data with hands-on mastery of tools like RDKit or OpenEye.
- A scientist who speaks the language of experimental drug discovery, with a strong familiarity with common assay types (biochemical/binding/cell-based assays, in vivo studies, etc.) and CADD workflows (docking, virtual screening, ADME prediction, etc.).
- A rigorous data scientist, with experience inmodeling and analysis of small molecule datasets and passion for statistical validation, uncertainty quantification, and deriving clear insights from complex, noisy data.
- A hands-on applied scientist and software engineer with strong coding skills in Python and a deep practical knowledge of the applied ML toolkit (e.g., scikit-learn, PyTorch).
- An exceptional communicator and collaborator, able to act as the bridge between machine learning researchers and experimental scientists.
- 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.
- A true team player 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
- A PhD in Cheminformatics, Computational Chemistry, Computer Science, or a related field.A track record of publications applying machine learning to drug discovery challenges.
- Deep expertise in advanced modeling techniques such as graph neural networks, multitask modeling, active learning, or Bayesian optimization.
- Experience with large-scale data management, including SQL databases and data pipelining tools.
- Strong opinions on molecule featurization and model validation.
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.
Genesis Molecular AI is pioneering a transformative approach to drug discovery by leveraging state-of-the-art machine learning, computational chemistry, and biology. Our mission is to accelerate the development of life-changing therapies by merging cutting-edge technology with innovative science. We are building a world-class team to drive forward the future of drug discovery.
About the Team:
We are a mission-driven group of scientists and engineers using cutting-edge computational methods to discover drugs for unmet medical needs. Our work spans machine learning, computational chemistry, biology, and wet lab experimentation, all operating within a high-trust, deeply technical environment.
You’ll join a team with extremely strong ML talent that is actively pushing the frontier of AI in drug discovery. We are refining how experimental data, biological context, and computational models integrate into a seamless feedback loop.
You will help reshape how data flows from experiment → infrastructure → model → decision and increase the speed and scale at which we learn.
The Role
This is a builder–integrator role inside a deeply technical, cross-domain environment spanning AI/ML, engineering, computational chemistry, biology, and wet lab operations. You will serve as the connective tissue and acceleration engine across these domains.
You will work closely with senior leadership to own roadmap definition and execution across critical internal platforms while driving structural improvements in how our teams operate.
This role exists to:
Bridge domain silos and reduce friction between ML experimentation and biological context
Operate at both the strategic systems level and the fiddly operational detail level
Get hands dirty when needed
Close the AI ↔ Chem loop so lab data is captured in standardized form, accessible for model training, and drives measurable model performance improvements
Increase iteration speed by an order of magnitude from research idea to validated result
Standardize execution across programs so we can scale programs without reinventing workflows
The products you’ll work on include:
Nucleus, our internal platform that houses the GEMS AI system that enables all of our drug discovery programs.
Our computational methods research platform – the data, analysis and pipelining platform that powers new physics and AI methods development.
You will own the product roadmap, prioritize initiatives, and drive the execution of projects that support our mission to revolutionize drug discovery.
Responsibilities
Roadmap Ownership: Define, maintain, and communicate the technical and scientific product roadmap in alignment with company goals, ensuring prioritization of impactful projects.
User research and design: Deeply understand user needs, translate workflow complexity into simple and effective product design.
Team Collaboration and Translating Across Domains: Facilitate effective collaboration among software engineers, ML researchers, and computational chemistry scientists, acting as a glue to bridge technical and scientific perspectives.
Stakeholder Engagement: Act as the primary point of contact for internal stakeholders, gathering input and communicating progress effectively.
Data-Driven Decision Making: Leverage data and feedback to make informed decisions and iterate on product features.
Product Strategy: Translate company objectives into actionable product plans, ensuring alignment with user needs and strategic goals.
Execution Leadership: Drive projects from concept to completion, ensuring high-quality deliverables on time and within scope.
Risk Management: Identify potential risks in product development and proactively implement mitigation strategies.
Within 12 months, success in this role should look like:
A functionally closed AI ↔ Chem loop
Measurable performance gains from internal data
Faster model–lab feedback cycles
Standardized, scalable execution playbooks replacing heavy customization
Direct contribution to major method launches by bridging scientific insight and product execution
Who you are:
5+ years of product management experience in a technical or scientific environment
Bachelor’s degree in Computer Science, Engineering, Chemistry, or related field (advanced degree preferred)
Strong understanding of machine learning systems and data infrastructure
Literacy in drug discovery, structural biology, or computational chemistry
Proven ability to operate at the interface of ML and life sciences
Demonstrated experience translating computational results into actionable scientific outcomes
Experience leading complex, cross-functional initiatives
Mindset: Passionate about innovation, problem-solving, and making a tangible impact on human health.
You are someone who:
Runs toward ambiguity rather than away from it
Knows what you don’t know and does not bluff in technical domains
Thinks in systems and anticipates what will not scale
Balances strategic thinking with hands-on execution
Is motivated by advancing methods that impact real patients
*Experience in biotech, pharma, frontier AI, or computational research environments is strongly preferred.
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.
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.
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