Engineer II /Senior Software Engineer, Simulation

About the role

Autonomous Science Simulation Platform

Lila Sciences is constructing autonomous science platforms designed to compress the cycle time of discovery. The Robotics and Scheduling teams own the scheduling and coordination infrastructure powering the AI Science Factory. They command instruments, manage robotic fleets, and maximize throughput across complex, multi-step laboratory processes. We are seeking a Software Engineer to build and maintain a discrete event simulation platform. This platform acts as a fast, lightweight complement to our high-fidelity robotics simulations. It serves as the primary testbed for iterating on scheduling strategies, fleet coordination algorithms, and capacity planning. This testbed exercises the same core decision-making code that runs in production, but at a fraction of the cost and time. You will partner closely with the Robotics and Scheduling teams. This collaboration ensures the simulation remains a trustworthy, reproducible tool for driving real engineering decisions.

What You Will Build

You will design and maintain a low-fidelity discrete event simulation platform. This platform models laboratory processes and the movement of assets. You will architect the platform so production scheduling and fleet coordination code integrates with minimal changes. The system will provide experiment tracking and reproducibility infrastructure. Every run is logged, versioned, and made comparable for future analysis. You will develop tooling for parameter sweeps, scheduler benchmarks, and capacity planning. This covers various lab configurations and workload profiles. You will partner with Scheduling and Robotics teams to validate new strategies. Finally, you will ship clean, well-documented simulation APIs. These APIs allow other engineers to build tools without deep knowledge of internal scheduling logic.

What You Need to Succeed

Success requires 1-3 years of hands-on experience in simulation engineering or operations research. Strong software engineering fundamentals are essential, including testing, CI/CD, and API design. You must balance velocity with maintainability. A strong understanding of experiment reproducibility is necessary. This includes seeding, versioning, logging, and structured comparison of simulation runs. You need a solid grasp of system modeling concepts. These concepts include queuing, resource contention, event scheduling, and stochastic behavior. Strong communication skills are required for working across teams. You must collaborate effectively with colleagues from diverse technical backgrounds.

Bonus Points For

Practical experience with discrete event simulation frameworks, such as SimPy, is valuable. Experience designing and maintaining internal developer tools or platforms is a plus. Background in robotics, warehousing, or logistics environments is beneficial. Familiarity with scheduling algorithms or heuristics is an advantage. This includes greedy dispatching, priority rules, and constraint-based approaches. Experience with experiment tracking platforms like MLflow or similar tools is welcome. Comfort with reasoning about tradeoffs between simulation fidelity, speed, and coverage of production code paths is essential.

Compensation and Benefits

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. This includes medical, dental, and vision coverage. We provide employer-paid life and disability insurance. You will enjoy flexible time off with generous company-wide holidays. Paid parental leave is included. An educational assistance program supports your growth. Commuter benefits include bike share memberships for office-based employees. We also offer a company-subidized lunch program.

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Designing autonomous science workflows at Lila Sciences where you own the simulation testbed.

Architecting low-fidelity discrete event models that mirror production scheduling logic without rewriting core engines.

Establishing experiment tracking so every run remains versioned, logged, and comparable for capacity planning reviews.

Guiding robotics teams through coordinated validation, ensuring fleet coordination strategies behave as expected inside simulation.

Defining lab workflows, instrument states, and robotic transport rules that drive repeatable, structured comparison across configurations.

Shipping clean simulation APIs so other engineers can build tools without understanding every internal scheduling detail.

Confirming reproducibility through seeding, versioning, and structured logging that survives configuration changes over time.

Requiring 1-3 years of simulation engineering or operations research experience with strong testing and API design fundamentals.

Demanding solid grasp of queuing, resource contention, event scheduling, and stochastic behavior within complex lab environments.

Expecting strong communication skills when working across robotics, scheduling, and platform teams with varied technical backgrounds.

Confirming details What you'll do

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