<p></p> <p><strong>Who we are</strong></p> <p><strong>Lab37 Robotics</strong><strong> </strong>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. </p> <p> </p> <p><strong>About the role</strong></p> <p>Own the forecasting and optimization models that turn Lab37's operational data into automated decisions: how much to prep, when and how much to refill each robot, and how to schedule labor against demand. The core challenge is decision-making under uncertainty: committing to prep quantities before demand is known, then scheduling constrained, perishable production against those commitments — a stochastic optimization problem with a probabilistic-forecasting front-end. You'll turn ambiguous, constraint-driven problems into structured models and drive them from concept to production, balancing accuracy, calibration, interpretability, and impact.</p> <p> </p> <p><strong>What you'll do</strong> </p> <p><strong>Operations Research & Optimization</strong></p> <ul> <li>Formulate and solve the core planning problem as optimization under uncertainty: prep, refill, and labor decisions coupled through shared, perishable, capacity-constrained production.</li> <li>Define objectives over the real tradeoffs (waste, stockouts, labor, quality, timing) under hard perishability constraints, and derive the service levels they imply rather than hand-tuning them.</li> <li>Match method to problem across closed-form, heuristic, and exact approaches (LP/MIP/CP), knowing when decomposition, duality, or a full solver is warranted, and when it's overkill.</li> <li>Build optimizers that continuously re-plan and that operators can read, trust, and override, with overrides captured as signal.</li> <li>Turn today's heuristics into constraint-aware optimizers, and prove the gain with walk-forward backtests that price the tradeoffs in operational and dollar terms.</li> </ul> <p><strong>Forecasting</strong></p> <ul> <li>Develop probabilistic demand forecasts for high- and low-volume components.</li> <li>Produce calibrated forecasts and correct censored demand where stockouts hide true demand.</li> <li>Solve cold-start forecasting for new locations with little or no history.</li> </ul> <p><strong>Cross-functional collaboration</strong></p> <ul> <li>Partner with operations, product, and engineering to translate data into crisp requirements; partner with data engineering on the inputs your models depend on.</li> <li>Communicate complex concepts clearly to technical and non-technical stakeholders, with explicit assumptions and tradeoffs.</li> <li>Support business operations with data-driven insight and analysis.</li> </ul> <p> </p> <p><strong>What we are looking for</strong></p> <ul> <li>Advanced degree (PhD or MS) is required in Operations Research, Industrial/Systems Engineering, Statistics, Computer Science, or a related quantitative field.</li> <li>3+ years in operations research, optimization, forecasting, applied modeling, or equivalent depth from graduate research.</li> <li>Demonstrated depth in optimization under uncertainty: you've formulated and solved stochastic, mixed-integer, scheduling, or inventory problems on real, messy data. Duality/shadow prices, decomposition, and solver tuning (Gurobi, CPLEX, CP-SAT/OR-Tools) expected.</li> <li>Probabilistic forecasting with a decision-focused mindset: calibration, quantile accuracy, censoring correction, per-location bias, and cold-start.</li> <li>Bias toward shipping: comfortable putting a pragmatic, good-enough model into production and improving it iteratively.</li> <li>Strong analytical and statistical rigor; able to diagnose performance, find root causes, and quantify tradeoffs.</li> <li>Strong Python and SQL; production-ready code; comfort with an optimizer/solver.</li> <li>Systems thinking; able to model complex, constraint-driven environments. </li> <li>Commitment to interpretability: plans and forecasts operators can trust, debug, and override, not black boxes.</li> </ul> <p><strong>Desirable skills</strong></p> <ul> <li>Stochastic programming, robust optimization, or scheduling with sequence-dependent setups.</li> <li>Real-time operational or telemetry data in production.</li> <li>Food service, on-demand delivery, or just-in-time manufacturing.</li> <li>Shipping models behind an API with engineering.</li> </ul> <p> </p> <p><strong>Why join us</strong></p> <ul> <li>Demand for online food delivery is growing really fast! In the last 5 years, just in the US, the overall market has expanded 10X from $10B to $100B, and could expand to $500bn- $1T by 2030.</li> <li>Changing the restaurant industry: You’ll be part of a team that helps restaurants succeed in online food delivery. </li> <li>Collaborative environment: You will receive support and guidance from experienced colleagues and managers, helping you to learn, grow and achieve your goals, and you’ll work closely with other teams to ensure our customer’s success.</li> </ul> <p> </p> <p><strong>What else you need to know</strong></p> <p>This role is based in our Los Angeles<em> </em>office. As a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That’s why all of our office-based teams work onsite, five days a week.</p> <p>The base salary range for this role is<strong> $182,000 - $230,000 per year.</strong></p> <p>Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.</p> <p>Base salary is just