Job description

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 USD About 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.

Requirements

Must-haves

Your Impact at LILA
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.
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.

Nice-to-haves

Bonus Points For

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlpythonaikubernetesci/cdllm

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Parental Leave