Advanced Autonomous Motion Planning Engineer

2 weeks ago

Singapore beBeeMotion Full time $180,000 - $250,000
About the Role

Lead research and development of novel algorithms for motion planning in autonomous driving, including advanced search-based methods, sophisticated geometry-based methods, and decision-making under uncertainty with a strong emphasis on probabilistic approaches.

Architect and integrate complex combinations of motion planning and prediction algorithms, driving their evaluation and refinement for real-world deployment.

Design and build a robust, scalable, and high-performance codebase that facilitates rapid exploration, prototyping, and rigorous evaluation of innovative motion planning approaches and algorithms.

Drive technical collaboration and interface seamlessly with perception and prediction components upstream and trajectory optimization, tracking and control components downstream, ensuring end-to-end system performance.

Leverage deep software development and research expertise to teach others better software practices and principles, fostering a culture of technical excellence.

Guide and mentor junior team members, cultivating a culture of product-focused engineering, rigorous research, and advanced development.

Key Requirements
  • Education: PhD preferred in Robotics, Computer Science, Computer Engineering, Mechanical Engineering, or a related field; or a Master's degree with 2+ years of experience in robotics (preferably AV industry)
  • Research Experience: 5+ years of research experience in robotics / motion planning, with a proven track record of contributing to state-of-the-art solutions
  • C++ Software Development: 3+ years of C++ software development, with an emphasis on developing high-performance and reliable systems
  • Technical Leadership: Past experience owning and leading technical development on complex features from problem formulation through research, implementation, and deployment in a production environment
  • Personal Qualities: Thirst for knowledge, continuous innovation, and a drive to push the boundaries of autonomous driving technology
Bonus Requirements
  • Probabilistic Models: Experience with probabilistic models, including Gaussian mixture models, Hidden Markov Models, and Particle Filters
  • Bayesian Modeling: Experience with Bayesian modeling and inference techniques for decision-making under uncertainty
  • Bazel Build Framework: Experience with the Bazel build framework

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