Senior Software Engineer, Scientific System of Record

Senior Software Engineer, Scientific System of Record

Cambridge, MA USA;
San Francisco, CA USA

Join us in shaping the future of science! We are seeking Senior Software Engineers with full stack experience to join our Scientific System of Record Team (SSR), where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting‑edge tools for automated scientific analysis and more. If you thrive in a collaborative, fast‑paced environment and bring best practices in git, development workflows, and user‑centered design, we want to hear from you!

The Scientific System of Record Team (SSR) builds the memory layer for Lila's operations. It answers two questions: what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design‑Build‑Test‑Learn (DBTL) loop.

• Lab Execution and Scientific Workflows: Build systems that model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows.
• User Interfaces and APIs: Design and implement high‑quality, secure, and well‑documented UIs and APIs that support scientists, automation systems, ML workflows, and AI‑driven applications.
• Application Development: Build front‑end and backend services with a focus on performance, maintainability, and reliability.
• Data and System Modeling: Develop domain models, schemas, indexes, and data contracts across SQL, No
  
  SQL, vector databases, data lake houses, and other scientific data systems.
• Reliability, Performance, and Scale: Diagnose bottlenecks, improve system performance, and contribute to observability, reliability, and operational excellence for production systems.
• Cloud and Infrastructure: Use AWS services, Kubernetes, and modern Dev Ops practices to build and deploy production‑grade systems.
• Cross‑Functional
  
  Collaboration:
  
  Partner with scientists, ML researchers, platform engineers, data engineers, automation teams, and product managers to translate scientific and operational needs into software.
• Engineering Quality: Contribute to architecture discussions, code reviews, testing practices, documentation, and shared engineering standards.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 4‑6+ years of engineering experience building and deploying large‑scale systems in production. You must be strong in either front‑end or backend.
• Strong expertise in at least one of the following areas, with the ability to work across the stack: front‑end engineering, backend engineering, or data modeling and system design.
• Type Script, React, and Python: Strong experience building modern applications with React and Type Script;
  Python experience is strongly preferred.
• Application and API Development: Experience designing, building, and maintaining APIs, services, and application components with a focus on reliability, performance, and maintainability.
• Databases and Data Modeling:
  
  Experience with SQL and at least one of No
  
  SQL, vector databases, search systems, or data lakehouse architectures; familiarity with schema design, indexing, and query optimization.
• Production Systems: Experience operating production software, including debugging, monitoring, performance tuning, and improving reliability over time.
• Collaboration: Strong communication skills and a track record of working cross‑functionally with engineers, product teams, scientists, or other domain experts.
• Problem Solving: Ability to take ownership of ambiguous technical problems, make practical trade‑offs, and deliver maintainable solutions.
• Hands‑on experience using AI coding assistants or AI‑augmented engineering workflows to improve productivity.

• Cloud and Dev Ops: Hands‑on experience with AWS, GCP, or Azure; strong understanding of Kubernetes, containerization, infrastructure as code such as Terraform or Cloud Formation, and CI/CD pipelines such as Git Hub Actions.
• Orchestration Systems: Experience with…