Your Impact at LILA

Lila Sciences is building autonomous science platforms that compress the cycle time of scientific discovery. The Robotics and Scheduling teams develop the scheduling and coordination infrastructure that drives our AI Science Factory, commanding instruments, managing robotic fleets, and maximizing throughput across complex, multi-step lab workflows.

We are looking for a Software Engineer to build and maintain a discrete event simulation platform that serves as a fast, lightweight complement to our high-fidelity robotics simulations. Your work will be the primary testbed for iterating on scheduling strategies, fleet coordination algorithms, and capacity planning, exercising the same core decision-making code that runs in production at a fraction of the cost and time. You will work closely with the Robotics and Scheduling teams to ensure the simulation remains a trustworthy, reproducible tool for driving real engineering decisions.

What You'll Be Building

• Design and maintain a low-fidelity discrete event simulation platform modeling lab workflows, instrument states, and robotic transport.
• Architect the platform so production scheduling and fleet coordination code runs with minimal changes.
• Build experiment tracking and reproducibility infrastructure so every run is logged, versioned, and comparable.
• Develop tooling for parameter sweeps, scheduler benchmarks, and capacity planning across lab configurations and workloads.
• Partner with Scheduling and Robotics teams to validate new scheduling and coordination strategies.
• Ship clean, well-documented simulation APIs other engineers can build on.

What You'll Need to Succeed

• 1-3 years of hands-on experience in simulation engineering, operations research, or a closely related field.
• Strong software engineering fundamentals: testing, CI/CD, API design, and the ability to balance velocity with maintainability.
• Strong understanding of experiment reproducibility: seeding, versioning, logging, and structured comparison of simulation runs.
• Solid grasp of system modeling concepts: queuing, resource contention, event scheduling, and stochastic behavior.
• Strong communication skills and comfort working across teams with different technical backgrounds.

Bonus Points For

• Practical experience with discrete event simulation (e.g. SimPy).
• Experience designing and maintaining internal developer tools or platforms.
• Experience in robotics, warehousing, or logistics environments where physical transport is a first-class part of the system model.
• Familiarity with scheduling algorithms or heuristics (greedy dispatching, priority rules, constraint-based approaches).
• Experience with experiment tracking platforms (MLflow, Weights & Biases, or similar).
• Comfort reasoning about the tradeoffs between simulation fidelity, speed, and coverage of production code paths.