Applied AIML Evaluation & Monitoring Manager, Autonomous Vehicle

Job Description

As an Applied ML Evaluation & Monitoring Manager on the Software Validation team within the AV organization you will lead a team focused on building and operating ML-driven evaluation tools systems and executions that enable safe comfortable and intuitive autonomous driving at scale. Your team will transform the way we monitor the changes in the end-to-end ML stack for Autonomous Vehicles designing and deploying centric tool sets which makes performance monitoring seamless and automated and enables scalable and deep causal analysis to get to the roots of problem space for effective engineering iterations.

You will partner closely with AI/ML engineers simulation engineers systems engineers and product managers to design implement and maintain robust evaluation and validation processes that combine simulation and on-road data. Your work will drive systematic data-driven improvements to AV software performance and help connect day-to-day monitoring and triage work to the engineering roadmap for continuous improvement and burn down of problem space.
This role combines hands-on technical leadership in applied ML evaluation with people management and team building. You will grow and mentor a diverse team of engineers shape the roadmap for evaluation capabilities and foster a culture of ownership curiosity and high-quality execution.

What Youll Do

• Lead and grow an Applied ML team responsible for designing implementing and operating scalable evaluation monitoring and deep-triage systems for AV ML stacks

• Own end-to-end ML Monitoring & Triage tool sets : from design to early prototypes experiment setup and final produtionization.

• Develop AI/ML-powered triage agents that dives deep into performance issues and generates solution level triage and root cause

• Partner cross-functionally with AI/ML simulation systems safety and product teams to identify the most important signals and scenarios to evaluate and to prioritize improvement opportunities for the ML stack.

• Create clear compelling narratives and reports that synthesize complex data into actionable insights for senior leaders and partner teams enabling informed timely launch and continuous deployment decisions.

• Foster a high-performance inclusive team culture focused on accountability rigorous thinking healthy debate and continuous learning.

• Establish and refine team processes (prioritization planning execution incident reviews) to ensure reliable scalable and repeatable delivery of evaluation results.

Your Skills & Abilities

• Bachelors degree in Computer Science Electrical/Computer Engineering Robotics Applied Mathematics or a related technical field or equivalent practical experience.

• 2 years of people management experience leading engineering validation or applied ML teams.

• 5 years of experience in one or more of: applied ML systems engineering validation/verification or evaluation for complex software or autonomy systems.

• Demonstrated end-to-end ownership from problem definition through methodology design implementation analysis and driving concrete product outcomes.

• Proven analytical and systems engineering skills in highly complex ambiguous technical domains (e.g. end-to-end ML stacks robotics or distributed systems).

• Experience designing or operating evaluation or validation pipelines that leverage both simulation and real-world data.

• Strong communication skills with a track record of aligning diverse stakeholders and presenting complex technical concepts to mixed technical and non-technical audiences.

What Will Give You A Competitive Edge

• Graduate degree (MS/PhD) in a relevant technical field (e.g. ML robotics control statistics systems engineering).

• Experience building or leading teams in autonomous vehicles robotics or safety-critical systems.

• Hands-on experience with ML-based autonomy systems including familiarity with model evaluation failure analysis and performance debugging in real-world conditions.

• Track record of growing and mentoring engineers including performance management career development and building inclusive diverse teams.

• Comfortable working in fast-paced highly ambiguous environments with the ability to balance strategic thinking and hands-on execution.

Compensation

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