Engineer II​/Senior Software Engineer, Simulation

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.

• 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.

• 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.

• Practical experience with discrete event simulation (e.g. Sim Py).
• 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.

We offer competitive base compensation with bonus potential and generous early‑stage equity. Your final offer will reflect your background, expertise, and expected impact.

Full‑time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer‑paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

Full‑time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.

-based positions; international salaries are set to local market.

$120,000 — $192,000 USD

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard‑coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at…