Motional
Principal Engineer Motion Planning
$240K–$330K
Boston, Massachusetts, United States +5 · Remote · Principal · Full-time · Posted Jul 21, 2025
- Location
- Boston, Massachusetts, United States
Pittsburgh, Pennsylvania, United States
Remote U.S.
Boston, Massachusetts, United States
Pittsburgh, Pennsylvania, United States
Remote - Experience
- 10+ years of research experience in robotics / motion planning; 5+ years of C++ software development. Alternatively, a Master's degree with 7+ years of experience in robotics is stated as an education path.
- Education
- PhD preferred in Robotics, Computer Science, Computer Engineering, Mechanical Engineering, or a related field; or a Master's degree with 7+ years of experience in robotics (preferably AV industry).
- Visa sponsorship
- Participates in E-Verify; all newly hired employees are queried to verify identity and employment eligibility.
About the job
Leads research, development, and production deployment of motion planning and control systems for autonomous vehicles operating in complex environments.
What you'll do
- Lead development and evaluation of motion-planning algorithms and integrate them with prediction, perception, optimization, tracking, and control systems.
- Monitor system performance, set technical strategies, and lead cross-functional projects and interface development.
- Build scalable, high-performance software and mentor teams in research and software engineering practices.
What they ask for
- 10+ years of research experience in robotics / motion planning.
- 5+ years of C++ software development focused on high-performance, reliable systems.
- Experience owning and leading complex technical features from problem formulation through research, implementation, and production deployment.
Nice to have
- Experience with probabilistic models, such as Gaussian mixture models, Hidden Markov Models, and Particle Filters, and machine learning for decision-making under uncertainty.
- Experience with the Bazel build framework.
Pay
$240,000–$330,000 USD base salary
Tools
C++, Bazel, Gaussian mixture models, Hidden Markov Models, Particle Filters
AI focus
Applies probabilistic approaches and machine learning, including Bayesian modeling and inference, to motion planning and decision-making under uncertainty.
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