PhD (or MS + 4+ yrs) in CS, EE, Applied Math, Physics, or similar field.
Expertise in model-based reinforcement learning, offline reinforcement learning, or hierarchical reinforcement learning.
Demonstrated proficiency in strong LLM development using PyTorch and scalable training/evaluation pipelines.
Experience in AI applications for science, automation, or human-robotic interfaces.
Responsibilities
Design and develop advanced AI systems for scientific applications.
Train and deploy AI agents for sequential decision-making and multi-step reasoning.
Create workflow and code generation capabilities from natural language inputs.
Develop benchmarks and test suites for evaluating AI agents' capabilities.
Measure progress towards scientific goals through telemetry and evaluation strategies.
Benefits
Hybrid and on-site work options based on team needs.
Opportunity to work alongside experts in biology, materials science, and automation.
Contribute to groundbreaking scientific research with AI.
Full Job Description
What You'll Be Building
Agentic AI for science: Systems that perform sequential decision-making and multi-step reasoning to solve domain-specific problems.
Workflow/code generation: From natural language intent to typed, executable steps for lab instruments.
Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals.
What You'll Need to Succeed
PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science).
Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains.
Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks.
Comfort collaborating across disciplines and interfacing with simulations and real lab systems.
Bonus Points For
Building long-horizon agents or RL for control/decision-making; experience with model-based or offline RL.
Designing domain-specific benchmarks and evaluation harnesses for complex scientific tasks.
Digital-twin development, calibration, and sim-to-real transfer.
Publications or open-source contributions in AI for science (especially publications in top-tier conferences like NeurIPS, ICML, AAAI, ICLR).
Location
San Francisco, CA or Cambridge, MA (Hybrid and On-Site available depending on team needs).
Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
