Staff Software Engineer, AI Lab Execution System

Lila Sciences•San Francisco, MA

1d•$192,000 - $238,000

About The Position

We are seeking a Staff Software Engineer - AI Lab Execution System to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize intelligent, data-driven applications front-end. You will focus on developing UI, services, high-performance APIs, databases, and ensuring the reliability of services 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 database, data lake houses, and vector databases. 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, and elegant systems, we would love to hear from you!

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 or data modeling and design.
Typescript, React and Python Expertise: Strong experience with React and Typescript is required.
Expertise in Databases: Strong experience with SQL, NoSQL, and emerging database technologies (e.g., Vector DBs); proven track record in schema design, indexing, and query optimization.
API Development: 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 drive productivity is required.
Scientific or Data-Intensive Domains: Experience working with life sciences, material sciences, or other research-heavy fields.
Communication & Collaboration : Acute 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.
Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Nice To Haves

Cloud & DevOps Knowledge: Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
Orchestration Systems: Experience with orchestration tools (Flyte, Temporal, Airflow, Prefect, etc.).

Responsibilities

Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.