Staff Software Engineer, Scientific System of Record
Lila Sciences · Cambridge, MA USA; San Francisco, CA USA · Alewife, Cambridge, MA · San Francisco, CA
CHECKED 14H AGO
Bio & Science AIonsitestaff$144k–$288k
At a glance
Highlights
- Build the memory layer connecting scientific intent to physical reality
- Help close the Design-Build-Test-Learn loop for autonomous discovery
- Build AI-driven platform executing the full scientific method at scale
What they offer
- Bonus potential and early-stage equity
- Medical, dental and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with company holidays and paid parental leave
- Educational assistance program
- Commuter benefits and subsidized lunch program
Ideal candidate
A collaborative systems thinker energized by scientific impact, comfortable owning complex problems across front-end and backend and partnering with researchers to make discovery reproducible.
Requirements
- Bachelor's or Master's in Computer Science, Engineering, or related field
- 6-8+ years building and deploying large-scale production systems
- Strong expertise across front-end, backend, or data modeling and system design
- Strong experience with React and TypeScript; Python strongly preferred
- Strong experience with SQL, NoSQL and vector databases, schema design and query optimization
- Proven ability to design and scale RESTful or GraphQL APIs
- Hands-on experience using AI coding assistants
- Experience in life sciences, materials science, or other data-intensive research domains
- Strong cross-functional communication with scientists, engineers and product teams
- Proven ownership of complex technical trade-offs across scalability and maintainability
Lab profile
About Lila Sciences
Building an autonomous science platform where AI, automation, and biology co-evolve for medicine and materials.
What they build
- Autonomous Science Platform
- AI-designed biologics validated in wet-lab
- Electro-optic materials and photonic devices
Known for
- Large-scale automated data generation
- High-throughput protein purification and analytics
- Protein design and BioML modeling
- Robotic orchestration and lab simulation