Operations Research Engineer
- Extreme Ownership – We take full responsibility for outcomes, relentlessly driving toward solutions.
- Engineer Out Requirements, then Automate – We simplify, optimize, and then automate for scale.
- Share Your Legos – We collaborate openly, share knowledge, and empower each other to build bigger, better solutions.
- Develop ML- and optimization-driven prediction and planning algorithms for dynamic, uncertain operational environments.
- Formulate open-ended operational problems into structured optimization or ML tasks.
- Build, train, and evaluate deep learning models used for prediction, planning, and decision-making.
- Apply advanced techniques including reinforcement learning, stochastic optimization, robust optimization, combinatorial optimization, and integer/mixed-integer programming.
- Develop scalable systems for routing, fleet optimization, dispatch algorithms, multi-agent planning, and scheduling.
- Integrate prediction and planning models into production software systems used for real-time operations.
- Debug complex behaviors, analyze model performance, and drive continual improvements in autonomy and optimization pipelines.
- Collaborate across autonomy, software, and operations teams to ensure solutions meet safety, efficiency, and reliability requirements.
- BS in Computer Science, Mathematics, Machine Learning, Robotics, Optimization, Operations Research, or a related field.
- 3+ years of experience in ML modeling, optimization, robotics, or autonomous planning systems.
- Proficiency in Python and C++ with strong analytical and debugging capabilities.
- Expertise in one or more of the following areas:
- Stochastic optimization, stochastic programming, chance constraints, uncertainty modeling
- Reinforcement learning, planning under partial observability, Bayesian decision-making
- Routing, scheduling algorithms, VRP, dynamic routing, fleet optimization, dispatch optimization
- Multi-agent systems, queueing systems, traffic-aware routing, service network modeling
- Experience deploying ML or optimization systems in production environments.
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