Principal Engineer Tech Lead Manager, ML Acceleration

About the role

You will lead a team dedicated to pushing the boundaries of machine learning performance within the critical domain of autonomous driving systems. In this capacity, you will own the technical vision required to accelerate inference workloads and reduce computational overhead across the entire software stack. You will guide engineers in implementing solutions that directly translate to safer and more efficient vehicle operations. This role sits at the intersection of software engineering and hardware interaction, defining how complex neural networks execute on diverse compute platforms. You will be responsible for managing the engineering lifecycle, from initial design and prototyping through to deployment and long-term maintenance of performance-critical modules. Success in this position will be measured by the tangible improvements in speed, efficiency, and reliability of the models that power self-driving capabilities. You will act as a key bridge between research innovation and production reality, ensuring that cutting-edge techniques are robustly integrated into the Motional stack. This position is central to advancing Motional's self-driving technology by ensuring that computational resources are used to their maximum potential. You will provide leadership that aligns engineering execution with strategic objectives, ensuring the ML Acceleration team delivers measurable value. The role demands a high degree of ownership, requiring you to anticipate challenges and drive initiatives that keep Motional at the forefront of autonomous driving performance.

Key facts

What you'll do

• Architect and implement strategies to enhance ML model inference speed and efficiency across the autonomous driving stack.
• Analyze complex performance bottlenecks and design low-level optimizations for machine learning workloads.
• Evaluate and integrate hardware-specific capabilities to maximize throughput for AI inference tasks.
• Lead the design and maintenance of performance analysis tools that provide deep visibility into ML execution.
• Partner with research teams to translate novel algorithms into production-ready, high-performance code.
• Define and enforce coding standards and best practices for performance-critical software components.
• Mentor engineers on advanced concepts in parallel computing, memory management, and numerical optimization.
• Collaborate with cross-functional partners to align ML acceleration initiatives with broader product and system goals.
• Drive the creation of infrastructure for benchmarking and regression testing of ML performance metrics.
• Ensure that all solutions maintain scalability and reliability under demanding real-world conditions.
• Guide the selection and adaptation of frameworks such as TensorFlow, PyTorch, and ONNX Runtime for optimal execution.
• Oversee the implementation of solutions that reduce latency and power consumption for onboard computing platforms.
• Conduct code reviews and technical design sessions to maintain high standards of quality and performance.
• Monitor industry trends to identify new optimization techniques and tools relevant to ML acceleration.
• Facilitate knowledge sharing sessions to elevate the technical capabilities of the entire team.

Requirements

• Hold a Bachelor's degree in Computer Science, Engineering, or a closely related technical field.
• Bring a minimum of 8 years of professional software engineering experience in demanding technical environments.
• Demonstrate at least 3 years of experience in a technical leadership or people management capacity.
• Show a proven track record with ML model optimization strategies and mainstream frameworks like TensorFlow, PyTorch, and ONNX Runtime.
• Exhibit a strong grasp of hardware acceleration paradigms including GPUs, TPUs, and specialized AI accelerators.
• Be highly proficient in C++ and Python, with a deep understanding of their performance characteristics.
• Have experience writing low-level, performance-sensitive code that interfaces with complex computational libraries.
• Possess the ability to read and interpret technical specifications for hardware components and driver interfaces.
• Demonstrate strong problem-solving skills and the ability to debug complex, multi-layered systems issues.
• Have a history of successfully delivering projects on schedule and within scope in a fast-paced environment.
• Exhibit excellent communication skills, capable of conveying technical concepts to both technical and non-technical stakeholders.
• Show a commitment to best practices in software engineering, including version control, testing, and documentation.

Nice to have

• Hold a Master's or PhD in a relevant technical discipline with a focus on computational science or engineering.
• Bring experience with embedded systems and the unique constraints of real-time performance requirements.
• Have familiarity with the architectural patterns and software stacks common in autonomous vehicle platforms.
• Possess a deep understanding of compiler optimizations and how they impact ML model performance.
• Experience with cloud-based ML training and inference infrastructure.
• Knowledge of quantization, pruning, and other model compression techniques.
• Familiarity with safety-critical systems and their development lifecycle.
• Experience mentoring junior engineers and conducting technical interviews.
• Understanding of networking protocols and their impact on distributed ML systems.
• Demonstrated ability to contribute to open-source projects related to ML frameworks or performance tools.