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. Your work will empower scientists with cutting-edge AI tools and accelerate research through:

• Model-centric innovation: Improving LLMs’ capabilities to tackle complex scientific problems (e.g., literature synthesis, autonomous experimentation, rigorous derivations).

• Cross-disciplinary collaboration: Partnering with Natural sciences domain experts to translate AI advancements into real-world scientific impact.

What you will do

• 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.

• AI/ML Expertise: 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 the minimum extensive experience in computer science and ideally experience scaling synthetic data generation.

• State-of-the-art Knowledge: Familiarity with the latest advancements in LLM architectures and post training.

• Technical Depth: Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques (e.g., fine-tuning, RLHF).

• Scientific Capability: Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge) to design effective AI solutions.

• Engineering Rigor: Proficiency in scientific computing (e.g., numerical methods, HPC) and software best practices.

• Collaboration: Experience translating technical AI concepts for non-experts (e.g., scientists, engineers) and iterating based on user needs.

• Mindset: Low ego, team player, and eager to tackle operational challenges (e.g., data pipelines, model deployment).