Simulation Engineer

Who we are

Lab37 Robotics is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our industry. 

About the role

Lab37 is hiring an industrial engineer specializing in discrete-event simulation to optimize robotic food-production systems. You will model end-to-end production flow (including equipment, labor, material movement, queues, capacity, and operating policies) to identify bottlenecks, evaluate system designs, and guide capital and operational decisions. Beginning with our Bowl Builder and expanding across the full kitchen, your models will help teams understand tradeoffs and make confident decisions about what to build next.

You will own the technical direction for industrial-engineering simulation at Lab37, including choosing tools, setting modeling standards, conducting time studies, characterizing empirical inputs, validating models against operating data, and integrating simulation into product and operations planning. Working closely with our hardware, robotics, and operations teams in Pittsburgh, you will turn complex physical systems into trusted decision-making tools. This is a senior/staff-level individual contributor role with the opportunity to establish a capability central to how Lab37 designs, tests, deploys, and operates its systems.

What you'll do 

Industrial engineering and simulation

• Build discrete-event simulation models of the Bowl Builder and broader kitchen systems, representing equipment, labor, material flow, queues, buffers, failures, and operating policies under realistic demand.
• Map production processes, conduct time studies, and characterize stochastic inputs—including order arrivals, cycle and setup times, failures and repairs, driver arrivals, demand, and labor variability—from operational data.
• Extend models from a single robot to end-to-end, multi-station kitchen flow, including prep, fulfillment, labor, material movement, queues, and buffers.
• Evaluate commercial platforms such as AnyLogic and Simio alongside SimPy, Arena, and in-house approaches; consolidate existing efforts and establish modeling standards.
• Validate models against operational results and telemetry, including known gaps in sensor fidelity, so predictions are accurate and trusted enough to guide decisions.