Chemist with Python Proficiency - AI Trainer

We're building a talent pool of Chemistry professionals with Python proficiency to contribute to project-based AI development initiatives, focused on evaluating and enhancing frontier AI models.

Designed for chemistry specialists who enjoy deep technical problem-solving, this pipeline role is for those looking to apply their computational expertise to evaluate and push the boundaries of frontier AI models, relying on domain-specific tools such as GROMACS, PySCF, ORCA, Quantum ESPRESSO, or AutoDock Vina, with verifiable, code-graded answers run inside isolated Linux environments.

Key Responsibilities

• Identify an appropriate computational chemistry package and build problems whose solution genuinely hinges on that tool's core capabilities, whether force fields, integrators, electronic-structure methods, basis sets, or pseudopotential workflows.
• Develop full Python solutions for each problem, providing all necessary input files, molecular geometries, and crystal structure definitions as required.
• Establish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the chemical context.
• Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10% and 30%.
• Tune molecular geometries, basis sets, and convergence thresholds iteratively, building an understanding of how the model navigates complex simulation environments.
• Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission.

Core Requirements

• Academic background in Chemistry or a closely related field.
• At least 2 years of hands-on experience in chemistry research, applied work, or teaching.
• Solid Python skills, applied to writing and validating computational solutions.
• Capacity to build problems that cannot be solved without specialized computational chemistry software.
• Excellent written and verbal communication skills in English.
• Ability to work independently in a remote, fast-paced environment.

Nice-to-Have

• Working knowledge of one or more domain-specific computational chemistry tools, including but not limited to GROMACS, LAMMPS, PySCF, ORCA, xtb, CREST, Quantum ESPRESSO, phonopy, pymatgen, AutoDock Vina, or GNINA, or a demonstrated ability to get up to speed independently.
• Prior exposure to how frontier AI models approach complex simulation tasks.
• Knowledge spanning more than one chemistry domain, such as molecular dynamics, quantum chemistry, or molecular docking.
• Familiarity with containerized or sandboxed Linux execution environments.

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.

We're building a talent pool of Chemistry professionals with Python proficiency to contribute to project-based AI development initiatives, focused on evaluating and enhancing frontier AI models.

Designed for chemistry specialists who enjoy deep technical problem-solving, this pipeline role is for those looking to apply their computational expertise to evaluate and push the boundaries of frontier AI models, relying on domain-specific tools such as GROMACS, PySCF, ORCA, Quantum ESPRESSO, or AutoDock Vina, with verifiable, code-graded answers run inside isolated Linux environments.

Key Responsibilities

• Identify an appropriate computational chemistry package and build problems whose solution genuinely hinges on that tool's core capabilities, whether force fields, integrators, electronic-structure methods, basis sets, or pseudopotential workflows.
• Develop full Python solutions for each problem, providing all necessary input files, molecular geometries, and crystal structure definitions as required.
• Establish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the chemical context.
• Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10% and 30%.
• Tune molecular geometries, basis sets, and convergence thresholds iteratively, building an understanding of how the model navigates complex simulation environments.
• Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission.

Core Requirements

• Academic background in Chemistry or a closely related field.
• At least 2 years of hands-on experience in chemistry research, applied work, or teaching.
• Solid Python skills, applied to writing and validating computational solutions.
• Capacity to build problems that cannot be solved without specialized computational chemistry software.
• Excellent written and verbal communication skills in English.
• Ability to work independently in a remote, fast-paced environment.

Nice-to-Have

• Working knowledge of one or more domain-specific computational chemistry tools, including but not limited to GROMACS, LAMMPS, PySCF, ORCA, xtb, CREST, Quantum ESPRESSO, phonopy, pymatgen, AutoDock Vina, or GNINA, or a demonstrated ability to get up to speed independently.
• Prior exposure to how frontier AI models approach complex simulation tasks.
• Knowledge spanning more than one chemistry domain, such as molecular dynamics, quantum chemistry, or molecular docking.
• Familiarity with containerized or sandboxed Linux execution environments.

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.