Principal Engineer Tech Lead Manager, ML Acceleration

Motional•San Francisco, CA

1d•$240,000 - $330,000

About The Position

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services. You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement. What you'll be doing: Team Leadership & Management: Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration. Provide technical guidance, mentorship, and career development opportunities to team members. Foster a collaborative and inclusive team environment. Define team goals, priorities, and roadmap in alignment with company objectives. Technical Strategy & Execution: Define the technical vision and strategy for ML acceleration across the organization. Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques. Design, develop, and implement scalable and efficient ML acceleration solutions. Cross-functional Collaboration: Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly. Communicate complex technical concepts and strategies to both technical and non-technical stakeholders. Act as a technical expert and advocate for ML acceleration initiatives across the company. Impact & Measurement: Regularly measure and report on the impact of acceleration efforts. Continuously seek opportunities for further optimization and innovation.

Requirements

Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems, infrastructure, or performance optimization.
3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
Deep expertise in ML frameworks such as PyTorch or JAX.
Proficiency in performance profiling and optimization techniques for ML workloads.
Strong programming skills in Python; C++ experience is a plus.
Knowledge of MLOps principles and practices.
Exceptional leadership, communication, and interpersonal skills.
Ability to attract, hire, and retain top engineering talent.
Proven ability to drive complex technical projects from conception to completion.
Strong problem-solving skills and a proactive, results-oriented mindset.
Ability to thrive in a fast-paced, dynamic environment.

Responsibilities

Team Leadership & Management: Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
Provide technical guidance, mentorship, and career development opportunities to team members.
Foster a collaborative and inclusive team environment.
Define team goals, priorities, and roadmap in alignment with company objectives.
Technical Strategy & Execution: Define the technical vision and strategy for ML acceleration across the organization.
Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
Design, develop, and implement scalable and efficient ML acceleration solutions.
Cross-functional Collaboration: Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
Act as a technical expert and advocate for ML acceleration initiatives across the company.
Impact & Measurement: Regularly measure and report on the impact of acceleration efforts.
Continuously seek opportunities for further optimization and innovation.