Lila Sciences
Research Engineer, Scientific Data & Workflow Infrastructure
- Confirmed live in the last 24 hours
- $148k–$240k
- Mid level
- Full-time
- On-site · Cambridge, MA USA; San Francisco, CA USA
- Added 2 months ago
About this role
Lila is seeking a Research Engineer to build and maintain the infrastructure underpinning its scientific discovery platform. This role involves designing and implementing a shared provenance system, creating reusable libraries, and ensuring reproducibility of research workflows. You'll collaborate closely with scientists and platform teams to create robust, scalable tools for scientific data management.
What you'll do
- Design and build a queryable provenance system for tracking scientific inputs, code, models, and results.
- Develop data models and metadata schemas for diverse scientific tools.
- Create reusable Python libraries and APIs for interacting with shared storage and metadata systems.
- Transform ephemeral agent traces into durable, queryable data records.
- Transition research prototypes into production-grade, scalable services.
- Establish engineering conventions for CI, testing, packaging, and release practices.
What they're looking for
- Python
- Data Modeling
- API Design
- Workflow Orchestration
- Kubernetes
- GPU Computing
Benefits
- Medical, dental, and vision coverage
- Life and disability insurance
- Flexible time off and paid holidays
- Paid parental leave
- Educational assistance program
- Company subsidized lunch program
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Lila Sciences
Lila Sciences builds AI and automation systems for scientific research, including tools for automated analysis, control systems for lab operations, and large language models for scientific tasks. The company is hiring software engineers, machine learning scientists, research engineers, and automation specialists to develop and maintain these scientific computing platforms.
- Industry
- Technology & Software
Likely interview questions
- Describe a time you designed a data model for a complex system. What challenges did you encounter?
- How would you ensure reproducibility in a scientific workflow that involves multiple tools and dependencies?