Your Impact at LILAThe 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.
CompensationWe 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
