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Research Engineer, Lab Automation
Periodic Labs · Menlo Park, California, United States
$200k–250k
25 days ago
♡
Research Engineer - Midtraining
Periodic Labs · Menlo Park, California, United States
$250k–350k
29 days ago
♡
Research Engineer, Semiconductor
Periodic Labs · Menlo Park, California, United States
$220k–300k
32 days ago
♡
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
$225k–325k
280 days ago
♡
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
$225k–325k
280 days ago
♡
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
$225k–325k
280 days ago
♡
Research Scientist, Condensed Matter Theory
Periodic Labs · United States, United States
$225k–325k
301 days ago
♡
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Research Engineer, Lab Automation
Periodic Labs · Menlo Park, California, United States
Pay
$200k–250k
Setting
On-site
We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.
About the Role
Join our team of scientists and engineers building a lab where AI and automation speed up materials discovery.
We're building an autonomous lab to speed up materials discovery, and we need someone who understands materials R&D from the inside — someone who's run the experiments, fought with the instruments, and knows what "the workflow" actually means at the bench. As our Research Engineer, you'll work directly with scientists to understand what they're trying to learn, then translate that into the hardware setups, instrument sequences, and engineering requirements that make it possible to automate.
What You'll Do
- Work closely with bench scientists to understand experimental goals and turn them into concrete hardware and workflow requirements.
- Evaluate, select, and configure lab instrumentation and hardware to support new and existing materials R&D workflows.
- Design experimental and automation workflows that hold up to the realities of materials synthesis and characterization.
- Serve as the technical bridge between scientists and the automation/software engineering team, making sure integration specs reflect how the science actually works.
- Troubleshoot instrument and workflow issues that require materials domain knowledge to diagnose.
- Collaborate with AI and data scientists to help shape how experimental data is structured and used for analysis and planning.
You Will Thrive in This Role If You Have
- PhD in Materials Science, Chemistry, Chemical Engineering, or a related field (or equivalent research experience).
- Strong working knowledge of common materials lab hardware (e.g. furnaces, fluid/gas handling manifolds, characterization tools, synthesis equipment) and the workflows built around them.
- Ability to communicate fluently with both scientists and engineers, and to translate scientific intent into clear technical requirements.
- Comfort writing Python to interact with instruments, manipulate experimental data, and prototype automation workflows
Especially Strong Candidates May Also Have
- Prior experience working alongside automation or software engineers.
- Experience with electronic lab notebooks, LIMS, or other lab data systems.
- A track record of designing or adapting experimental protocols for higher-throughput or automated execution.
Mechanics
- Minimum education: PhD or equivalent combination of education and hands-on research experience
- Location: Menlo Park, CA (Soon: San Francisco, too)
- Compensation: $200,000-$250,000 + equity
- Visa sponsorship: Yes, we sponsor visas.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Engineer - Midtraining
Periodic Labs · Menlo Park, California, United States
Pay
$250k–350k
Setting
On-site
We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and a drive to push the boundaries of what's scientifically possible.
ABOUT THE ROLE
We're training frontier models to develop deep scientific knowledge and reasoning for scientific discovery. As a Midtraining Research Engineer, you'll take base models and improve their scientific reasoning: curating and generating data, building evals, and running large-scale training experiments. Your work will also lay the groundwork for our pre-training efforts down the line.
WHAT YOU'LL DO
- Identify, process, and curate novel sources of scientific data for large-scale model training.
- Generate high-quality synthetic data to fill gaps in scientific knowledge and reasoning.
- Build evaluations that correlate with downstream scientific task performance, working closely with RL researchers, physicists, and chemists.
- Develop and apply techniques such as self-distillation and on-policy distillation to improve model capability.
- Design and run large-scale training experiments, partnering with supercompute engineers to scale efficiently across thousands of GPUs.
- Build tools for yourself and the team to investigate how data choices shape model intelligence.
YOU WILL THRIVE IN THIS ROLE IF YOU HAVE
- Experience training LLMs on curated mixes of trillions of tokens.
- Experience on a dedicated evals team supporting a large production training run.
- Hands-on use of self-distillation, on-policy distillation, or similar methods in a real training pipeline.
- Experience with scaling laws and compute-optimal hyperparameters.
- Comfort working across data, evals, and training infrastructure.
ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE
- Experience optimizing throughput and reliability for large-scale distributed training runs.
- A background in AI for science or training on specialized domain data (e.g., protein, materials, or other scientific datasets).
- Experience creating evals or synthetic data for non verifiable tasks and tracking performance over live runs.
MECHANICS
- Minimum education: Bachelor's degree or similar experience
- Location: Menlo Park, CA (Soon: San Francisco, too)
- Compensation: $250,000–$350,000 + equity
- Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Engineer, Semiconductor
Periodic Labs · Menlo Park, California, United States
Pay
$220k–300k
Setting
On-site
As a Research Engineer, Semiconductor, you’ll own device-level validation of our materials work, taking novel thin-film stacks from deposition through patterned test vehicles to measure electrical performance.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
Pay
$225k–325k
Setting
On-site
Computational Scientist, Structural & Thermal
Location
Menlo Park, CA
Address
Menlo Park, California
Employment Type
Full time
Location Type
On-site
Department
Science
Overview
Application
About Periodic Labs
Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, semiconductors, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About the Role
Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to develop structural, thermal, and coupled thermo-mechanical simulation capabilities for semiconductor systems and advanced materials.
This role is for someone who thinks of themselves as both a scientist and a software engineer. You understand solid mechanics and heat transfer at a level that goes beyond configuring a commercial FEA package, and you are comfortable building, extending, or automating solvers when the physics or scale of the problem requires it.
What You'll Do
Develop agent-based structural and thermal simulation capabilities for semiconductor systems, including wafer stress and warpage, thin-film residual stress, thermo-mechanical reliability, thermal budget modeling, process-induced deformation, fracture and delamination, and coupled heat-stress problems.
Build or extend custom solvers where commercial FEA tools are too slow, too opaque, or insufficiently flexible. This may include custom FEM implementations, phase-field fracture models, crystal plasticity codes, thin-film mechanics frameworks, or reduced-order mechanical models, written in Python, C++, or Julia.
Model materials behavior at the level the physics requires: elasticity, plasticity, viscoelasticity, creep, fracture, diffusion-induced stress, thermal expansion mismatch, interfacial mechanics, and materials evolution under process conditions.
Validate models against experimental measurements including wafer metrology, curvature and bow measurements, DIC, profilometry, nanoindentation, or failure analysis data.
Design and curate evaluation datasets in collaboration with RL researchers to train LLMs capable of directing complex simulation pipelines.
Generate simulated datasets for ML training in regimes where experimental coverage is expensive or difficult to achieve.
Build and automate simulation pipelines at scale, architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops.
You Will Thrive Here If You Have
Periodic Labs is an early-stage startup, and we're looking for someone who can bring technical leadership to modeling structural and thermal behavior in semiconductor devices, not necessarily someone who already has every skill listed below. A strong growth mindset, demonstrated ownership, and a track record of getting up to speed quickly in new technical areas are much more important than experience in semiconductors.
A PhD or equivalent research experience in mechanical engineering, materials science, aerospace engineering, or a closely related field, with a strong foundation in solid mechanics and heat transfer. Early-career candidates with strong research or engineering output are encouraged to apply.
Hands-on experience with computational mechanics at the code level: writing or substantially modifying FEM codes, implementing constitutive models, or developing custom solvers for structural or thermal problems.
Familiarity with open-source simulation frameworks such as FEniCS, deal.II, MOOSE, or similar is a strong positive signal. Contributions to open-source projects that others actually use are even better.
Strong Python skills, with C++, Julia, or Fortran proficiency a plus. Experience running simulations programmatically at scale rather than through point-and-click tools.
Deep understanding of solid mechanics theory: continuum mechanics, tensor formulations, constitutive modeling, variational methods, and numerical methods for PDEs.
Experience using mechanics or thermal simulations to explain experimental observations, guide materials or process decisions, or surface failure mechanisms that were not obvious from measurement alone.
Genuine curiosity about AI and a desire to work at the boundary of simulation and machine learning.
Strong Candidates May Also Have
Experience with GPU-accelerated FEM, reduced-order mechanical models, surrogate models, or physics-informed neural networks for structural problems.
Familiarity with multiscale mechanics: bridging from atomistic or DFT-informed potentials through molecular dynamics, mesoscale methods, and continuum mechanics.
Deep expertise in thin-film mechanics: residual stress, Stoney equation regimes and their limits, film-substrate interactions, delamination, and stress evolution during deposition.
Experience with wafer-scale mechanics: bow, warp, thermal cycling reliability, packaging-induced stress, or interconnect mechanics in advanced packaging contexts.
Background in fracture mechanics or damage modeling: LEFM, cohesive zone models, phase-field fracture, fatigue, or statistical failure models.
Knowledge of semiconductor metrology, failure analysis, or process integration that gives physical intuition for how models connect to real manufacturing decisions.
Mechanics:
Minimum education: Bachelor's degree or similar experience
Location: Menlo Park, CA (Soon: San Francisco, too)
Compensation: $225,000-325,000 + equity
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
Pay
$225k–325k
Setting
On-site
Computational Scientist, Structural & Thermal
Location
Menlo Park, CA
Address
Menlo Park, California
Employment Type
Full time
Location Type
On-site
Department
Science
Overview
Application
About Periodic Labs
Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, semiconductors, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About the Role
Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to develop structural, thermal, and coupled thermo-mechanical simulation capabilities for semiconductor systems and advanced materials.
This role is for someone who thinks of themselves as both a scientist and a software engineer. You understand solid mechanics and heat transfer at a level that goes beyond configuring a commercial FEA package, and you are comfortable building, extending, or automating solvers when the physics or scale of the problem requires it.
What You'll Do
Develop agent-based structural and thermal simulation capabilities for semiconductor systems, including wafer stress and warpage, thin-film residual stress, thermo-mechanical reliability, thermal budget modeling, process-induced deformation, fracture and delamination, and coupled heat-stress problems.
Build or extend custom solvers where commercial FEA tools are too slow, too opaque, or insufficiently flexible. This may include custom FEM implementations, phase-field fracture models, crystal plasticity codes, thin-film mechanics frameworks, or reduced-order mechanical models, written in Python, C++, or Julia.
Model materials behavior at the level the physics requires: elasticity, plasticity, viscoelasticity, creep, fracture, diffusion-induced stress, thermal expansion mismatch, interfacial mechanics, and materials evolution under process conditions.
Validate models against experimental measurements including wafer metrology, curvature and bow measurements, DIC, profilometry, nanoindentation, or failure analysis data.
Design and curate evaluation datasets in collaboration with RL researchers to train LLMs capable of directing complex simulation pipelines.
Generate simulated datasets for ML training in regimes where experimental coverage is expensive or difficult to achieve.
Build and automate simulation pipelines at scale, architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops.
You Will Thrive Here If You Have
Periodic Labs is an early-stage startup, and we're looking for someone who can bring technical leadership to modeling structural and thermal behavior in semiconductor devices, not necessarily someone who already has every skill listed below. A strong growth mindset, demonstrated ownership, and a track record of getting up to speed quickly in new technical areas are much more important than experience in semiconductors.
A PhD or equivalent research experience in mechanical engineering, materials science, aerospace engineering, or a closely related field, with a strong foundation in solid mechanics and heat transfer. Early-career candidates with strong research or engineering output are encouraged to apply.
Hands-on experience with computational mechanics at the code level: writing or substantially modifying FEM codes, implementing constitutive models, or developing custom solvers for structural or thermal problems.
Familiarity with open-source simulation frameworks such as FEniCS, deal.II, MOOSE, or similar is a strong positive signal. Contributions to open-source projects that others actually use are even better.
Strong Python skills, with C++, Julia, or Fortran proficiency a plus. Experience running simulations programmatically at scale rather than through point-and-click tools.
Deep understanding of solid mechanics theory: continuum mechanics, tensor formulations, constitutive modeling, variational methods, and numerical methods for PDEs.
Experience using mechanics or thermal simulations to explain experimental observations, guide materials or process decisions, or surface failure mechanisms that were not obvious from measurement alone.
Genuine curiosity about AI and a desire to work at the boundary of simulation and machine learning.
Strong Candidates May Also Have
Experience with GPU-accelerated FEM, reduced-order mechanical models, surrogate models, or physics-informed neural networks for structural problems.
Familiarity with multiscale mechanics: bridging from atomistic or DFT-informed potentials through molecular dynamics, mesoscale methods, and continuum mechanics.
Deep expertise in thin-film mechanics: residual stress, Stoney equation regimes and their limits, film-substrate interactions, delamination, and stress evolution during deposition.
Experience with wafer-scale mechanics: bow, warp, thermal cycling reliability, packaging-induced stress, or interconnect mechanics in advanced packaging contexts.
Background in fracture mechanics or damage modeling: LEFM, cohesive zone models, phase-field fracture, fatigue, or statistical failure models.
Knowledge of semiconductor metrology, failure analysis, or process integration that gives physical intuition for how models connect to real manufacturing decisions.
Mechanics:
Minimum education: Bachelor's degree or similar experience
Location: Menlo Park, CA (Soon: San Francisco, too)
Compensation: $225,000-325,000 + equity
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Computational Scientist, Structural & Thermal
Periodic Labs · Menlo Park, California, United States
Pay
$225k–325k
Setting
On-site
ABOUT PERIODIC LABS
Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, semiconductors, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
ABOUT THE ROLE
Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to develop structural, thermal, and coupled thermo-mechanical simulation capabilities for semiconductor systems and advanced materials.
This role is for someone who thinks of themselves as both a scientist and a software engineer. You understand solid mechanics and heat transfer at a level that goes beyond configuring a commercial FEA package, and you are comfortable building, extending, or automating solvers when the physics or scale of the problem requires it.
WHAT YOU'LL DO
- Develop agent-based structural and thermal simulation capabilities for semiconductor systems, including wafer stress and warpage, thin-film residual stress, thermo-mechanical reliability, thermal budget modeling, process-induced deformation, fracture and delamination, and coupled heat-stress problems.
- Build or extend custom solvers where commercial FEA tools are too slow, too opaque, or insufficiently flexible. This may include custom FEM implementations, phase-field fracture models, crystal plasticity codes, thin-film mechanics frameworks, or reduced-order mechanical models, written in Python, C++, or Julia.
- Model materials behavior at the level the physics requires: elasticity, plasticity, viscoelasticity, creep, fracture, diffusion-induced stress, thermal expansion mismatch, interfacial mechanics, and materials evolution under process conditions.
- Validate models against experimental measurements including wafer metrology, curvature and bow measurements, DIC, profilometry, nanoindentation, or failure analysis data.
- Design and curate evaluation datasets in collaboration with RL researchers to train LLMs capable of directing complex simulation pipelines.
- Generate simulated datasets for ML training in regimes where experimental coverage is expensive or difficult to achieve.
- Build and automate simulation pipelines at scale, architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops.
YOU WILL THRIVE HERE IF YOU HAVE
- Periodic Labs is an early-stage startup, and we're looking for someone who can bring technical leadership to modeling structural and thermal behavior in semiconductor devices, not necessarily someone who already has every skill listed below. A strong growth mindset, demonstrated ownership, and a track record of getting up to speed quickly in new technical areas are much more important than experience in semiconductors.
- A PhD or equivalent research experience in mechanical engineering, materials science, aerospace engineering, or a closely related field, with a strong foundation in solid mechanics and heat transfer. Early-career candidates with strong research or engineering output are encouraged to apply.
- Hands-on experience with computational mechanics at the code level: writing or substantially modifying FEM codes, implementing constitutive models, or developing custom solvers for structural or thermal problems.
- Familiarity with open-source simulation frameworks such as FEniCS, deal.II, MOOSE, or similar is a strong positive signal. Contributions to open-source projects that others actually use are even better.
- Strong Python skills, with C++, Julia, or Fortran proficiency a plus. Experience running simulations programmatically at scale rather than through point-and-click tools.
- Deep understanding of solid mechanics theory: continuum mechanics, tensor formulations, constitutive modeling, variational methods, and numerical methods for PDEs.
- Experience using mechanics or thermal simulations to explain experimental observations, guide materials or process decisions, or surface failure mechanisms that were not obvious from measurement alone.
- Genuine curiosity about AI and a desire to work at the boundary of simulation and machine learning.
STRONG CANDIDATES MAY ALSO HAVE
- Experience with GPU-accelerated FEM, reduced-order mechanical models, surrogate models, or physics-informed neural networks for structural problems.
- Familiarity with multiscale mechanics: bridging from atomistic or DFT-informed potentials through molecular dynamics, mesoscale methods, and continuum mechanics.
- Deep expertise in thin-film mechanics: residual stress, Stoney equation regimes and their limits, film-substrate interactions, delamination, and stress evolution during deposition.
- Experience with wafer-scale mechanics: bow, warp, thermal cycling reliability, packaging-induced stress, or interconnect mechanics in advanced packaging contexts.
- Background in fracture mechanics or damage modeling: LEFM, cohesive zone models, phase-field fracture, fatigue, or statistical failure models.
- Knowledge of semiconductor metrology, failure analysis, or process integration that gives physical intuition for how models connect to real manufacturing decisions.
MECHANICS:
- Minimum education: Bachelor's degree or similar experience
- Location: Menlo Park, CA (Soon: San Francisco, too)
- Compensation: $225,000-325,000 + equity
- Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Scientist, Condensed Matter Theory
Periodic Labs · United States
Pay
$225k–325k
Setting
Remote
Research Scientist, Condensed Matter Theory
Location
United States
Address
United States
Employment Type
Full time
Location Type
Remote
Department
Science
Overview
Application
About Periodic Labs
We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About the Role
Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale.
As a Research Scientist in Condensed Matter Theory, you will use theoretical modeling to connect first-principles calculations and experiments. You will collaborate closely with computational and experimental scientists and ML researchers to develop physical understanding that guides and accelerates the discovery of novel quantum materials.
What You’ll Do
Develop and apply theoretical models to interpret experimental observations and guide materials discovery efforts
Bridge first-principles calculations (e.g., DFT) and experimental results to build predictive physical understanding
Collaborate with ML researchers to incorporate theoretical insights into machine learning models and inform training data strategies
Work with computational and experimental scientists to design experiments and validate theoretical predictions
Communicate theoretical findings clearly across disciplines and contribute to a shared scientific roadmap
You Will Thrive in This Role If You Have
PhD in condensed matter theory, with a focus on quantum materials
Deep expertise in relating theoretical models to real materials and experimental observables
Strong publication record demonstrating impactful, independent research
Ability to collaborate effectively across theory, computation, and experiment
Especially Strong Candidates May Also Have
Experience running first-principles calculations such as density functional theory (DFT) on realistic systems
Experience with deep learning methods, including graph neural networks, applied to materials or physics problems
Experience modeling superconductivity and/or magnetism in quantum materials
Familiarity with high-throughput computational workflows or materials databases
Mechanics
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)
Compensation: $225,000–$325,000 + equity
Visa sponsorship: Yes, we sponsor visas.
We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Scientist, Condensed Matter Theory
Periodic Labs · United States, United States
Pay
$225k–325k
Setting
Remote
About Periodic Labs
We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About the Role
Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale.
As a Research Scientist in Condensed Matter Theory, you will use theoretical modeling to connect first-principles calculations and experiments. You will collaborate closely with computational and experimental scientists and ML researchers to develop physical understanding that guides and accelerates the discovery of novel quantum materials.
What You’ll Do
Develop and apply theoretical models to interpret experimental observations and guide materials discovery efforts
Bridge first-principles calculations (e.g., DFT) and experimental results to build predictive physical understanding
Collaborate with ML researchers to incorporate theoretical insights into machine learning models and inform training data strategies
Work with computational and experimental scientists to design experiments and validate theoretical predictions
Communicate theoretical findings clearly across disciplines and contribute to a shared scientific roadmap
You Will Thrive in This Role If You Have
PhD in condensed matter theory, with a focus on quantum materials
Deep expertise in relating theoretical models to real materials and experimental observables
Strong publication record demonstrating impactful, independent research
Ability to collaborate effectively across theory, computation, and experiment
Especially Strong Candidates May Also Have
Experience running first-principles calculations such as density functional theory (DFT) on realistic systems
Experience with deep learning methods, including graph neural networks, applied to materials or physics problems
Experience modeling superconductivity and/or magnetism in quantum materials
Familiarity with high-throughput computational workflows or materials databases
Mechanics
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)
Compensation: $225,000–$325,000 + equity
Visa sponsorship: Yes, we sponsor visas.
We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Scientist, Condensed Matter Theory
Periodic Labs · United States, United States
Pay
$225k–325k
Setting
Remote
ABOUT PERIODIC LABS
We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
ABOUT THE ROLE
Join a world-class team of scientists and engineers pushing the boundaries of physics research in a groundbreaking lab where AI, theory, and automation unlock discoveries at unprecedented speed and scale.
As a Research Scientist in Condensed Matter Theory, you will use theoretical modeling to connect first-principles calculations and experiments. You will collaborate closely with computational and experimental scientists and ML researchers to develop physical understanding that guides and accelerates the discovery of novel quantum materials.
WHAT YOU’LL DO
- Develop and apply theoretical models to interpret experimental observations and guide materials discovery efforts
- Bridge first-principles calculations (e.g., DFT) and experimental results to build predictive physical understanding
- Collaborate with ML researchers to incorporate theoretical insights into machine learning models and inform training data strategies
- Work with computational and experimental scientists to design experiments and validate theoretical predictions
- Communicate theoretical findings clearly across disciplines and contribute to a shared scientific roadmap
YOU WILL THRIVE IN THIS ROLE IF YOU HAVE
- PhD in condensed matter theory, with a focus on quantum materials
- Deep expertise in relating theoretical models to real materials and experimental observables
- Strong publication record demonstrating impactful, independent research
- Ability to collaborate effectively across theory, computation, and experiment
ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE
- Experience running first-principles calculations such as density functional theory (DFT) on realistic systems
- Experience with deep learning methods, including graph neural networks, applied to materials or physics problems
- Experience modeling superconductivity and/or magnetism in quantum materials
- Familiarity with high-throughput computational workflows or materials databases
MECHANICS
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)
Compensation: $225,000–$325,000 + equity
Visa sponsorship: Yes, we sponsor visas.
We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.
Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
Research Scientist, Thin Films
Periodic Labs · Menlo Park, California, United States
Pay
$225k–325k
Setting
On-site
Research Scientist, Thin Films
Location
Menlo Park, CA
Address
Menlo Park, California
Employment Type
Full time
Location Type
On-site
Department
Science
Overview
Application
About Periodic Labs
We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About the Role
Join a world-class team of scientists and engineers pushing the boundaries of materials research in a groundbreaking lab where AI and automation unlock discoveries at unprecedented speed and scale.
As a Research Scientist in Thin Film Materials Discovery, you will lead thin-film synthesis efforts using advanced PVD platforms, building the experimental foundation for autonomous discovery loops. You will collaborate with computational and AI scientists, and partner with engineers designing next-generation automated laboratory infrastructure.
What You’ll Do
Develop synthesis strategies to realize novel thin-film materials predicted by AI
Determine and control crystal structures, defects, microstructures, and properties of previously unrealized compounds
Partner with AI and computation teams to build predictive models of materials growth and properties grounded in physics and chemistry
Work with engineers to design, test, and deploy automated growth and characterization hardware
You Will Thrive in This Role If You Have
PhD in chemistry, physics, or materials science, with 5+ years of hands-on experience
Deep expertise with thin-film synthesis methods such as sputtering, PLD, and MBE, with demonstrated experience and creativity across diverse chemistries
Strong skills in structural and chemical characterization, particularly of thin films and materials with structures or compositions never before realized experimentally — including diffraction, microscopy, and spectroscopy
Experience probing the optical, electronic, magnetic, thermal, and/or other properties of thin films
Proven record of collaboration with computational groups, especially for high-throughput materials discovery
Demonstrated commitment to laboratory safety and stewardship, with hands-on experience in hazardous chemistries
Strong track record of highly impactful research demonstrated by publications in top-tier journals and/or inventions, and recognized leadership in the field
Especially Strong Candidates May Also Have
Experience working in national user facilities such as synchrotrons or neutron sources
Development of novel synthesis or characterization techniques
Comfort with large experimental datasets and analysis pipelines
Experience with automation and scripting in laboratory environments
Mechanics
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)
Compensation: $225,000-325,000 + equity
Visa sponsorship: Yes, we sponsor visas.
We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.
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Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.
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