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Senior Autonomy Engineer II
| Company | May Mobility |
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| Location | Ann Arbor, MI, USA |
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| Salary | $176000 – $210000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior |
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Requirements
- Master’s or PhD degree in Robotics, Computer Science, Computer Engineering, or a related field with strong mathematical and engineering foundations.
- A minimum of 3+ years of GPU programming/optimization using CUDA or similar techniques for perception algorithms and models
- Proficiency in C/C++/Python and experience in software development in Linux environments.
- Strong experience with GPU programming, CUDA, and real-time data processing.
- Experience optimizing ML models for runtime efficiency.
- Experience with 3D computer vision and point cloud processing.
Responsibilities
- Work closely with across functional teams to co-define software and system requirements, analyze trade-offs, and shape the future generation of compute platforms.
- Collaboratively integrate perception algorithms and machine learning models with vehicle hardware and software, ensuring seamless operation within autonomous driving systems.
- Collaborate with ML infrastructure teams to develop and optimize distributed training infrastructure, automate deployment pipelines, and enhance system reliability and performance.
- Conduct rigorous testing and validation of perception algorithms in both simulated and real-world environments to ensure robustness, reliability, and safety.
- Develop and optimize perception stack software using CUDA and GPU programming to accelerate computationally intensive tasks and maximize efficiency.
- Optimize machine learning models for runtime efficiency, scalability, and performance across GPU, TPU, and CPU architectures, ensuring adaptability to various vehicle platforms.
- Stay at the forefront of machine learning, GPU programming, and autonomous driving technologies, integrating the latest advancements into the development process.
- Actively participate in feature design, code reviews, debugging, and issue resolution, driving improvements in perception software performance.
Preferred Qualifications
- Expertise in ML/DL model optimization for real-time applications with limited compute resources.
- Strong contributions to deployed robotic systems, demonstrating field-proven capabilities in perception evaluation and testing.
- Experience with robotics middleware such as ROS (Robot Operating System).
- Knowledge of vehicle dynamics and control systems.
- Experience deploying ML models efficiently on embedded hardware.
- Familiarity with hardware acceleration techniques beyond GPUs, such as TPUs and FPGAs.
