Job Summary

May Mobility is entering an exciting phase of growth as we expand our first-of-its-kind autonomous shuttle and mobility services across the nation. Launched in 2017 with a strong team of experienced roboticists, perception, behavior, AI, and software engineers with decades of experience fielding robotic systems in the wild, May Mobility is looking to expand its team of perception engineers with a background in robotics or autonomous vehicles.

We are seeking an experienced Senior Engineer or above with deep expertise in perception systems, machine learning, and GPU optimization. As part of our team, you will play a critical role in enhancing perception’s on-vehicle capabilities, ensuring robust performance for real-time applications, and optimizing frameworks for autonomous vehicle perception.

Essential 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.
• Lead the efforts of ptimize 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.

Skills and Abilities

Success in this role typically requires the following competencies:

• Strong programming skills in C++ and Python with a deep understanding of software optimization.
• Extensive experience in optimizing ML models for resource-constrained inference, including custom operations, model pruning, quantization, and knowledge distillation.
• Expertise in GPU programming, particularly CUDA, for high-performance computing and efficient parallel processing.
• Proficiency in model-platform co-optimization, ensuring efficiency across GPU, TPU, and CPU architectures.
• Hands-on experience with real-time data processing and advanced optimization techniques.
• Strong background in ML model inference optimization, balancing accuracy and latency for real-time applications.
• Familiarity with machine learning frameworks and libraries for perception-related tasks.
• Excellent problem-solving skills with a detail-oriented approach and a rigorous testing mindset to ensure system reliability.

Qualifications and Experience

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

Required

• 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 in Senior engineer roles of GPU programing/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.

Desirable

• 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.

Physical Requirements

• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use

Travel required? - Moderate: 11%-25%