ML Research Scientist, Scientific Evaluations

Lila Sciences
Cambridge +4 more

Who this role is best for

A natural match if you have experience creating scientific evaluations and building complex data pipelines.

Best fit for

  • Candidates with a background in computational biology, chemistry, or materials science and a track record in creating model training environments
    — “Experience applying ML or computation to biology, chemistry, or material sciences.”
  • Individuals who excel at designing and prototyping scientific evaluation systems and environments
    — “Design and prototype new evals and environments for science — creating high-quality evaluations, identifying environment failures, and patching model capability gaps.”
  • Candidates with strong software engineering fundamentals and experience in end-to-end pipeline development
    — “Experience building large, complex data pipelines for model training or production.”

Things to consider

  • The role involves significant responsibility for integrating experimental work into shared infrastructure
    — “ability to integrate experimental work into shared infrastructure.”

How to stand out

  • Highlight experience with synthetic environment generation and validation in your resume and interview responses
    — “Build end-to-end pipelines for synthetic environment generation”
  • Demonstrate your ability to quickly prototype and iterate on scientific workflows using Python
    — “comfortable quickly building and iterating on new ideas in Python.”
  • Emphasize any prior work with distributed compute or LLM inference systems in your application materials
    — “Familiarity with distributed compute (Ray, Modal) or LLM inference systems (vLLM, batching strategies).”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • high-quality evaluations
  • environment designs
  • model training
Typical background
MLAIscientific evaluations

Skills & requirements

Required

MLAIScientific EvaluationsRL EnvironmentsData PipelinesPrototyping

Preferred

LLM TrainingPublicationsDistributed ComputeAsync Python

Stack & domain

MLComputationBiologyChemistryMaterial SciencesRL EnvironmentsData PipelinesPythonResearchProduction InfrastructureDistributed ComputeLLM TrainingAsync PythonPydanticWorkflow OrchestrationAIAutomationScientific DiscoveryLlmsSynthetic Environment GenerationReward ShapingData CollectionTraining LoopAI Science FactoryOperating System For ScienceScientific MethodMedicineMaterialsEnergyCreativityResearch IntuitionAmbiguity NavigationClean CodeVersion ControlTestingIntegrationExperiment DesignPublication Track RecordConference PresentationsResearch InfrastructureAsync ProgrammingScienceRLDistributed ComputingAI Research

About the role

Original posting from Lila Sciences via Greenhouse

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).

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range$176,000—$304,000 USDAbout LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

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.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

Source: Lila Sciences careers (Greenhouse)

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