Role Overview
This role focuses on defining and advancing learning-based approaches for motion planning in autonomous systems. It involves setting the technical direction and delivering solutions that evolve from traditional rule-based and optimization-driven methods toward data-driven techniques such as imitation learning, reinforcement learning, and diffusion-based planning.
The position combines hands-on technical leadership with ownership of the roadmap, ensuring that learning-based planners are not only innovative but also practical, reliable, and ready for real-world deployment. The emphasis is on delivering measurable improvements over existing planning approaches through robust engineering and evaluation.
Key Responsibilities
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Technical roadmap ownership
Define and execute a clear roadmap for learning-based planning over a 12–24 month horizon. Identify the most suitable methods, determine build vs. adopt decisions, and establish criteria for progressing solutions from prototype to production deployment.
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Model development and training
Design, train, and refine learning-based planning models using a range of approaches, including imitation learning, reinforcement learning (online and offline), diffusion models, and hybrid techniques. Select the most effective method based on the problem context.
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System integration
Integrate learning-based planners into existing planning and control architectures. Ensure clean interfaces with upstream perception systems and downstream control layers, along with reliable fallback mechanisms and production-grade robustness.
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Data strategy and requirements
Define data needs for training and evaluation, including sources such as human demonstrations, simulation, and existing planning outputs. Drive efforts to ensure sufficient coverage of scenarios, edge cases, and real-world variability.
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Evaluation and benchmarking
Develop comprehensive evaluation frameworks to assess learned planners against established baselines. Establish metrics, scenario-based testing, and failure analysis processes to ensure readiness for deployment.
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Iterative experimentation
Lead the full experimentation loop—from hypothesis and model development to real-world testing and performance analysis—ensuring continuous improvement based on empirical results.
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Hybrid planning strategy
Work closely with existing planning approaches to determine where learning-based methods provide value and where traditional techniques remain optimal. Contribute to designing hybrid systems that combine both effectively.
Required Experience
- 6+ years of experience in motion planning for autonomous systems or mobile robotics, including hands-on work with learning-based planning approaches
- Demonstrated experience taking learning-based planning solutions from concept through to deployment in real-world environments
- Strong understanding of classical planning approaches, including model predictive control (MPC), sampling-based, and optimization-based methods
- Proficiency in C++ and Python, with experience in ROS (ROS1 and/or ROS2)
- Experience with modern machine learning frameworks (e.g., PyTorch) and solid understanding of experimental design and validation
Preferred Qualifications
- Experience publishing or delivering solutions involving learning-based planning methods such as imitation learning, reinforcement learning, diffusion-based models, or world-model approaches
- Background in designing hybrid systems that combine learned components with classical planning or safety mechanisms
- Familiarity with building robust, real-world systems that operate under uncertainty and dynamic conditions
