Senior Software Engineer, Operations Research
Cambridge, MA USA On-site
$180,000–$256,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
We are seeking a Senior Software Engineer, Operations Research to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize backend systems and data infrastructure that power orchestration and lab execution. You will focus on developing services, high-performance APIs, databases, and ensuring the reliability of systems that integrate advanced AI frameworks with complex scientific workflows. You'll work closely with robotics researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale to demanding throughput. This is an opportunity to apply your engineering 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.
What you'll bring
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- 5–10 years of engineering experience building and deploying large-scale backend or data systems in production.
- Backend / Data Development: Experience developing distributed software and data systems (Postgres, Flyte, Temporal, NATS/MQTT, FastAPI).
- Hands-on experience using AI coding assistants to drive productivity is required.
- 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.
- Experience developing scheduling software or manufacturing execution systems.
- Experience with operations research solvers (OR-Tools, HiGHS, Gurobi).
- Familiarity with Python for Science: Familiarity with data science, data visualization, and ML libraries (pandas, polars, numpy, scipy, pytorch).
- Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
- Cloud & DevOps Knowledge: Hands-on experience with AWS