ML Research Scientist, Scientific Evaluations

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.

• 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.

• 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.

• 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).

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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