ML Research Scientist, Scientific Evaluations

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to accelerate scientific discovery. Our AI Research team is looking to hire a ML Research Scientist to focus on building scientific problems and environments to evaluate frontier LLMs on.

What You'll Be Building

• Design and prototype new evals and environments for science — creating high-quality evaluations, identifying environment failures, and patching model capability gaps.
• Invent new types of RL environments — explore creative ways to turn scientific workflows, simulators, and real-world tasks into training signal.
• Build end-to-end pipelines for synthetic environment generation — transforming raw assets (papers, codebases, lab protocols) into validated, versioned training environments at scale.
• Prototype and validate environment designs — from initial concept through reward shaping, data collection, and integration with the training loop.

What You'll Need to Succeed

• Experience applying ML or computation to biology, chemistry, or material sciences.
• A strong track record of creating environments or evaluations for model training
• Experience building large, complex data pipelines for model training or production.
• Strong prototyping and coding skills — comfortable quickly building and iterating on new ideas in Python.
• Creativity and research intuition — ability to identify promising directions, design experiments, and navigate ambiguity.
• Solid software engineering fundamentals — clean code, version control, testing, and ability to integrate experimental work into shared infrastructure.

Bonus Points For

• Demonstrated experience training large language models
• Strong publication track record at conferences such as NeurIPS, ICML, and ICLR.
• Strong Python programming skills, including research and production infrastructure
• Familiarity with distributed compute (Ray, Modal) or LLM inference systems (vLLM, batching strategies).
• Experience with async Python, Pydantic, and workflow orchestration frameworks (Flyte, Airflow).