About the Role
The role involves designing, implementing, and validating learnable planning algorithms that enable autonomous systems to navigate and make decisions in dynamic, real-world conditions.
Responsibilities
- Design and prototype planning algorithms that incorporate learning components
- Integrate planning modules into the full autonomy stack
- Collaborate with simulation and data teams to improve training environments
- Evaluate planner performance using real-world and synthetic scenarios
- Optimize decision-making behavior under uncertainty
- Work closely with machine learning engineers to refine model architectures
- Debug and resolve issues in planning logic during test cycles
- Develop metrics to assess planner safety and efficiency
- Support deployment of planning systems in test vehicles
- Refine system behavior based on field feedback
- Contribute to software infrastructure for planner training and evaluation
- Ensure robustness of planning outputs across edge cases
- Translate research concepts into production-grade code
- Participate in cross-team design reviews
- Maintain up-to-date documentation for planner components
- Improve computational efficiency of planning pipelines
- Assist in creating scenario suites for validation
- Analyze planner decisions using telemetry data
- Collaborate on safety case development for planning behaviors
- Stay current with advancements in AI-based planning methods
- Support integration with perception and control systems
- Contribute to versioning and testing of planner models
- Work on failure mode analysis and mitigation strategies
- Help scale planner training workflows
- Ensure compatibility with real-time system constraints
Nice to Have
- Master’s or PhD in computer science, robotics, or related field
- Prior work on autonomous vehicle planning systems
- Experience with reinforcement learning in planning contexts
- Publication record in robotics or AI conferences
- Hands-on experience with real-world robot deployment
- Knowledge of formal methods in safety assurance
- Familiarity with behavioral cloning or imitation learning
- Experience with large-scale data processing for training
- Background in probabilistic graphical models
- Contributions to open-source robotics projects
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid work model with flexibility for remote and in-office collaboration
Team
Part of a multidisciplinary AI and robotics team focused on autonomous freight solutions
About the Team
This role is embedded within a research-forward engineering group building next-generation autonomy for freight transportation. The team combines deep learning, classical planning, and high-fidelity simulation to create scalable, safe, and efficient systems.
What We Value
Technical rigor, creative problem solving, and a commitment to real-world impact. We prioritize candidates who can bridge theoretical concepts with practical implementation in complex environments.
Visa sponsorship available for qualified candidates
