Principal ML Systems Engineer, Data Platform (Autonomous Vehicles)

About the role

This role is categorized as hybrid. This means the successful candidate is expected to report to offices in Austin, TX, Mountain View, CA or the Greater Seattle Area three times per week, at minimum.

Responsibilities

• Design & develop the next generation distributed ML data platform (Ingestion, Processing, Serving) using GCP and open-source frameworks.
• Leading the strategy of building performant and efficient multi-cloud platforms.
• Collaborate with stakeholders (ML & Data Engineers), translate needs & pain points into requirements, build self-serve capabilities and drive adoption.
• Deliver e2e technical projects owning major technical decisions and tradeoffs & contribute to the team’s strategic roadmap.
• Champion engineering & operational excellence by continuously improving systems and processes.
• Participate in team’s planning, code reviews and design discussions.
• Conduct technical interviews, onboard new and mentor junior engineers.

Requirements

• BA or MS in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field.
• 10+ years in building distributed data platforms using major cloud providers and open-source frameworks.
• Expert-level proficiency in Java, C++, or Python, with a proven track record of designing and implementing robust, distributed systems.
• Expertise in implementing Data Processing Frameworks (Beam, Spark) and serving layers optimized for high-throughput, low-latency delivery.
• Experience optimizing services for cost efficiency, performance & reliability.
• Experience with Micro services architecture and proven ability to manage the full operational lifecycle of systems.
• Deep understanding of full ML model lifecycle (feature engineering, training, validation, deployment, monitoring, etc.).
• Strong passion, and understanding about self-driving technology and its potential impact on the world.
• Experience working with (100+) petabyte-scale ingestion, processing, and serving architectures.

Qualifications

• Master’s degree in Computer Science, Software Engineering, or related field.
• Building and optimizing large-scale cloud-native data platforms using GCP or similar cloud technologies.
• Experience in self-driving, autonomous vehicle, ADAS, or connected vehicle data domains.
• Ability to work cross-functionally with Data Science, ML, analytics, and product stakeholders to deliver production-ready data products.
• Familiarity with robustness, validation, and simulation for ML systems used in autonomous driving contexts.

Skills & Abilities

• Expert-level proficiency in Java, C++, or Python, with a proven track record of designing and implementing robust, distributed systems.
• Expertise in implementing Data Processing Frameworks (Beam, Spark) and serving layers optimized for high-throughput, low-latency delivery.
• Experience optimizing services for cost efficiency, performance & reliability.
• Experience with Micro services architecture and proven ability to manage the full operational lifecycle of systems.
• Deep understanding of full ML model lifecycle (feature engineering, training, validation, deployment, monitoring, etc.).
• Strong passion, and understanding about self-driving technology and its potential impact on the world.

Benefits

• Health and wellbeing benefit programs including medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts.

Pay

The expected base compensation for this role is: $233,400 - $339,650. Actual base compensation within the identified range will vary based on factors relevant to the position.

Schedule

This role is categorized as hybrid. This means the successful candidate is expected to report to offices in Austin, TX, Mountain View, CA or the Greater Seattle Area three times per week, at minimum.