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Lab37 Robotics is building digital twin and simulation capabilities to transform how they design and scale robotic food production systems. In this staff-level individual contributor role, you will own the technical direction for simulation across the company, including tool selection, modeling standards, empirical characterization, and validation against real operational data.
Your primary focus is building discrete-event simulation models of the Bowl Builder robot and expanding to the broader kitchen ecosystem—covering dispensing, timing, module configuration, throughput, multi-station flow, prep, and labor dynamics. You will characterize stochastic inputs (order arrivals, dispense timing, error rates, driver arrivals) from operational telemetry, then validate models against real data to ensure predictions are trusted enough to drive decisions.
You will run what-if analyses and configuration experiments that are slow, costly, or risky to test on live systems. Your models will quantify throughput SLAs and performance targets for proposed robots and modules, directly informing which hardware the company builds next. You'll support operational questions around equipment sizing, bottleneck analysis, and kitchen configuration tradeoffs.
Cross-functional collaboration is central: you'll partner closely with hardware and robotics engineering teams in Pittsburgh to ground models in actual machine behavior, then translate simulation results into clear, decision-ready recommendations for technical and non-technical stakeholders.
Required: Bachelor's degree in Industrial Engineering, Operations Research, Statistics, Computer Science, or related quantitative field (advanced degree a plus). 3+ years of relevant work experience in simulation, industrial engineering, operations research, or data science. Hands-on discrete-event simulation experience (AnyLogic, Simio, SimPy, Arena, or custom engines) is core to the role. You need a track record of models that drove real design or operational decisions, strong analytical and statistical rigor, strong programming skills (Python, SQL), and comfort partnering with hardware engineers using telemetry and operational data.
Desirable: experience with digital twins of physical/manufacturing systems, robotics/automation/manufacturing environments, queueing theory, throughput/bottleneck analysis, capacity planning, real-time operational data in production, or food service/on-demand delivery/just-in-time manufacturing systems.
