Research Engineer, Scientific Data & Workflow Infrastructure
At a glance
Mid-level AI Research role at Lila Sciences. Cambridge · $148,000–$240,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Create a shared record linking scientific inputs, code and model versions, runs, and results across research workflows
- Design schemas and metadata for runs, artifacts, and results produced by different scientific tools
- Build Python libraries and APIs so pipelines and agents can use shared storage and metadata
- Convert agent activity into durable records, and turn research scripts into maintainable production services
- Set engineering practices for testing, packaging, releases, and documentation across local, cloud, and GPU systems
What you bring
- You bring strong Python skills and a history of delivering systems that remain maintainable
- You have modeled evolving data with schemas, metadata, identifiers, and versioning
- You have designed APIs and built libraries that scientists or engineers rely on
- You have supported ML experiments, simulations, or data pipelines with reproducibility in mind
Who this fits
This mid-level role suits a Python engineer who enjoys building infrastructure alongside scientists and supporting research workflows. It is based in Cambridge, United States.
From the employer
Your Impact at LILA
Lila is building an operating system for science, powered by scientific superintelligence — generating hypotheses, designing and running experiments, and learning from new data in real time to speed up discovery in fields like therapeutics and advanced materials.
The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery: biophysics workflows, computational chemistry tools, cofolding models, low-data learning systems, simulation workflows, and agent-usable scientific pipelines. This role sits embedded with scientists, working closely with Lila’s platform teams on the storage, compute, and orchestration layers beneath it. You’ll build the provenance system that connects every scientific input, code version, model, run, and result produced across Lila’s simulation, ML, and agent-driven workflows — so a scientist can find a result months later, trace exactly how it was produced, and reuse it without reconstructing it from logs.
What You'll Be Building
- Shared provenance infrastructure — a queryable, trusted record connecting inputs, code/model versions, runs, and outputs across simulation, ML, and agent workflows.
- Data models and metadata schemas for runs, artifacts, and results that work across heterogeneous scientific tools.
- Reusable Python libraries and APIs that let tools, pipelines, and AI agents write to and read from shared storage and metadata systems.
- Structured records from agent activity — turning ephemeral agent traces into durable, queryable data.
- Production-grade tooling from research prototypes — taking scientific workflows from one-off scripts to scalable, maintainable services.
- Engineering conventions (CI, testing, packaging, release practices, documentation) that hold up across local, cloud, and GPU compute environments.
What You'll Need to Succeed
- Strong Python engineering skills with a track record of designing and shipping maintainable systems.
- Experience with data modeling — schemas, metadata, identifiers, versioning for data that changes over time.
- Experience designing APIs and building libraries that other engineers or scientists depend on.
- Experience supporting research workflows (ML experiments, simulations, data pipelines) with a clear sense of what makes results reproducible.
Bonus Points For
- Familiarity with chemistry, computational biophysics, molecular simulation, or drug discovery workflows.
- Experience with experiment tracking / data lineage tools (MLflow, Weights & Biases, OpenLineage, DVC).
- Experience with workflow orchestration (Flyte, Ray, Argo, Airflow, Temporal, Dagster).
- Experience with Kubernetes, containers, and CI/CD at scale.
- GPU computing experience, ideally at hundreds-to-thousands-of-GPU scale.
- Experience building tools for LLM agents or automated research workflows.
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$148,000—$240,000 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.