LLM Scientist, AI for Science

We are hiring an LLM Scientist to push the boundaries of AI-driven scientific discovery. You will specialize in developing and optimizing large language models (LLMs), agentic AI, and deep learning frameworks to solve open problems in the natural sciences: Biology, Chemistry, Physics, and/or Materials Science.

Responsibilities

AI/ML Research: Participate in end-to-end training of LLM-based solutions, with a focus on scalability, generalization, and scientific applicability; Data creation: Design original workflows to accumulate high quality scientific data at scale, either through synthetic or crowd sourced pipelines; Algorithm Design: Use and improve reinforcement learning, or agentic systems tailored to Natural sciences challenges (e.g., modeling resolution, material property prediction, advanced data analysis and coding); Interdisciplinary Bridge: Work closely with domain scientists to identify AI opportunities and refine models based on feedback; Tooling & Infrastructure: Contribute to company infrastructure to improve usability for model training in scientific contexts and open-source AI tools for science for non-AI experts; Autonomous Innovation: Set a research agenda for AI in science, exploring high-risk/high-reward ideas

Requirements

Strong background in LLMs, deep learning, or agentic AI, with a track record of applying these to real-world problems (publications, open-source contributions, or deployed systems); at minimum extensive experience in computer science; Familiarity with the latest advancements in LLM architectures and post training; Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques (e.g., fine-tuning, RLHF); Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge); Proficiency in scientific computing (e.g., numerical methods, HPC) and software best practices; Experience translating technical AI concepts for non-experts and iterating based on user needs; Low ego, team player, and eager to tackle operational challenges (e.g., data pipelines, model deployment)

Nice to have

Experience scaling synthetic data generation

Benefits

Healthcare coverage; Parental leave; Retirement plans; Relocation support; Wellness programs; Meal and transportation allowances; Other location-specific perks