ML Research Scientist, Scientific Evaluations

Alewife, Cambridge, MAfull timeOn Site

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$176k–$304k

H1B: no

Lila Sciences Lila Sciences is hiring an ML Research Scientist to build scientific problems and environments for evaluating frontier LLMs. The role includes designing new evals, inventing RL environments, and building pipelines for synthetic environment generation.

Responsibilities

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.

Qualification

Experience building largeStrong Python programming skillsExperience with async Python

Required

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.

Preferred

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).

Benefits

Dental
Vision
Equity/RSU
Parental Leave
Life Insurance

Company

Lila Sciences

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Source: greenhouse

Active within the past hour

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