Senior Software Engineer, Scientific System of Record

Lila Sciences•Cambridge, MA

4h•$144,000 - $240,000

About The Position

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.

Requirements

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.
TypeScript, React, and Python: Strong experience building modern applications with React and TypeScript; 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 NoSQL, 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.

Nice To Haves

Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes, containerization, infrastructure as code such as Terraform or CloudFormation, and CI/CD pipelines such as GitHub Actions.
Experience with orchestration tools such as Flyte, Temporal, Airflow, Prefect, or similar systems.
Experience building laboratory, scientific workflow, LIMS, ELN, data platform, or ML platform products.
Experience designing systems that support auditability, traceability, reproducibility, data provenance, or regulated workflows.

Responsibilities

Build systems that model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows.
Design and implement high-quality, secure, and well-documented UIs and APIs that support scientists, automation systems, ML workflows, and AI-driven applications.
Build front-end and backend services with a focus on performance, maintainability, and reliability.
Develop domain models, schemas, indexes, and data contracts across SQL, NoSQL, vector databases, data lakehouses, and other scientific data systems.
Diagnose bottlenecks, improve system performance, and contribute to observability, reliability, and operational excellence for production systems.
Use AWS services, Kubernetes, and modern DevOps practices to build and deploy production-grade systems.
Partner with scientists, ML researchers, platform engineers, data engineers, automation teams, and product managers to translate scientific and operational needs into software.
Contribute to architecture discussions, code reviews, testing practices, documentation, and shared engineering standards.