Staff Software Engineer building Cerebras’s production ML inference APIs. Enabling reliable model serving across GPU and Cerebras AI accelerator backends.
About the role
- Senior Software Engineer developing C++ and Python behavior-planning systems for AeroVect’s autonomous airport vehicles. Testing algorithms in simulation and real-world airside operations.
Responsibilities
- Develop and implement advanced behavior planning algorithms for autonomous vehicles
- Collaborate with cross-functional teams to ensure robust integration and functionality of planning systems
- Design, write, and maintain efficient and scalable code in C++ and Python
- Contribute to the architecture and continuous improvement of behavior planning software
- Conduct extensive testing in simulated environments and real-world scenarios to validate and refine behavior planning algorithms
- Analyze system performance and implement enhancements based on data and feedback
- Maintain comprehensive documentation of code, algorithms, and system designs
- Work closely with other engineering teams to ensure seamless coordination and development
- Own the design and implementation of key modules in the behavior planner
- Work closely with the autonomy engineering team and report to the Planning Tech Lead
Requirements
- Proficient in modern C++ (11/14/17) and object-oriented programming
- Skilled in Python for rapid prototyping and testing
- Strong in debugging, profiling, and optimizing code
- Deep understanding of behavior planning algorithms such as state machines, behavior trees, and probabilistic planning
- Familiarity with path planning algorithms like A*, RRT, or optimization-based methods
- Master’s degree in Computer Science, Robotics, or a related field
- Minimum of 3 years of industry experience in autonomous driving, robotics, or a related field
- Knowledge of machine learning techniques, especially in the context of behavior prediction and planning
- Experience with ROS / ROS2
- Experience implementing systems that can re-plan at high frequencies to adapt to dynamic changes in the environment
- Experience ensuring behavior planning algorithms can execute with minimal latency for real-time navigation
- Proficiency in optimization techniques and probabilistic models for making informed planning decisions under uncertainty
Benefits
- Equity
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