Lila Sciences

AI Science

EngineerII/SeniorSoftwareEngineer,Simulation

$120–192k Cambridge, Massachusetts, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Engineer II /Senior Software Engineer, Simulation at Lila Sciences. Skills: discrete event simulation, software engineering, scheduling algorithms, system modeling. Design and maintain simulation platform. Architect platform for production code”

What You'll Achieve.

maximizing throughput across complex, multi-step lab workflows; simulation remains a trustworthy, reproducible tool for driving real engineering decisions

Industry & Context.

AI Science
Problems you'll solve

scheduling strategies; fleet coordination algorithms; capacity planning

What They're Looking For.

Must Have

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

Nice to Have

Practical experience with discrete event simulation (e. g. SimPy), Experience designing and maintaining internal developer tools or platforms, Experience in robotics, warehousing, logistics environments, Familiarity with scheduling algorithms or heuristics, greedy dispatching, priority rules, constraint-based approaches

What You'll Do.

Design and maintain simulation platform

Architect platform for production code

Build experiment tracking infrastructure

Develop tooling for parameter sweeps

Partner with teams to validate strategies

How You'll Work.

Team & Collaboration

work closely with the Robotics and Scheduling teams; Partner with Scheduling and Robotics teams; comfort working across teams with different technical backgrounds

Communication Scope

communication skills; comfort working across teams with different technical backgrounds

Full Job Description

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 comp

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