Staff Software Engineer, Scientific System of Record

Lila Sciences•Cambridge, MA

7h•$144,000 - $288,000

About The Position

We are seeking a Staff Software Engineer to join our Scientific System of Record Team and help build the next-generation AI-driven scientific platform. You will focus on developing user interfaces, services, high-performance APIs, databases, and reliable systems that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. You’ll work closely with ML researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale seamlessly, including structured SQL databases, data lakehouses, workflow engines, and lab execution environments. This is an opportunity to apply your deep front-end and backend expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about building performant, elegant systems, we would love to hear from you.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
6–8+ years of engineering experience building and deploying large-scale systems in production.
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.
Strong experience with React and TypeScript is required; Python experience is strongly preferred.
Strong experience with SQL, NoSQL, and emerging database technologies such as vector databases; proven track record in schema design, indexing, and query optimization.
Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance.
Hands-on experience using AI coding assistants to improve engineering productivity.
Experience working in life sciences, materials science, or other research-heavy or data-intensive fields.
Strong listening skills and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
Proven ability to take ownership of complex technical challenges while balancing trade-offs between scalability, performance, and maintainability.

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

Design and build high-performance, secure, and well-documented UIs and APIs that integrate with AI-driven applications.
Develop schemas and manage diverse data systems, including SQL, NoSQL, vector databases, and other emerging technologies, for performance and scalability.
Drive implementation of front-end and backend services with a focus on performance, maintainability, and reliability.
Diagnose and resolve system bottlenecks while ensuring high availability and low-latency performance across large-scale workloads.
Leverage AWS services, Kubernetes, and modern DevOps practices to build and deploy production-grade systems at scale.
Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.