Research Engineer, Scientific Data & Workflow Infrastructure
$148k - $240k • Cambridge, MA USA; San Francisco, CA USA • Full-time
Posted 2mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Cambridge, MA USA; San Francisco, CA USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila Sciences is building an operating system for science, powered by scientific superintelligence. The Molecular Intelligence team focuses on building ML and physics-based infrastructure for drug discovery, including biophysics workflows, computational chemistry tools, and simulation workflows. This role involves embedding with scientists and collaborating with platform teams on storage, compute, and orchestration. The core of the work is building a provenance system to track scientific inputs, code versions, models, runs, and results across various workflows, enabling scientists to easily find, trace, and reuse discoveries.
Responsibilities
- Build shared provenance infrastructure for a queryable, trusted record of scientific workflows.
- Develop data models and metadata schemas for runs, artifacts, and results across heterogeneous scientific tools.
- Create reusable Python libraries and APIs for interacting with shared storage and metadata systems.
- Structure data from agent activity into durable, queryable records.
- Transition research prototypes into production-grade, scalable, and maintainable services.
- Implement engineering conventions for CI, testing, packaging, release, and documentation across different compute environments.
Requirements
- Strong Python engineering skills with experience designing and shipping maintainable systems.
- Experience with data modeling, including schemas, metadata, identifiers, and versioning.
- Experience designing APIs and building libraries for other engineers or scientists.
- Experience supporting research workflows (ML experiments, simulations, data pipelines) with a focus on reproducibility.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Comprehensive medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company-wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits
- Company subsidized lunch program