Staff ML Validation Applied Scientist

Descripción

Role 

As a  Staff ML Validation Applied Scientist  on the  Software Validation  team within the  Autonomous Vehicle (AV) organization , you will lead applied machine learning research focused on improving verification and validation of ML components and autonomous driving behavior at scale. 

You will push the frontier of  simulation-led ML validation , creating metrics, tools, and  agentic workflows  that make it dramatically faster, more automated, and more robust to evaluate autonomy systems across large fleets, diverse scenarios, and continuous release cycles. You will transform advanced ML research into working prototypes and production-grade validation services, including  AI validation critics  that automatically review model behavior, logs, and simulation traces to surface issues, regressions, and coverage gaps. 

About the Organization 

The  Autonomous Vehicle (AV) organization  is dedicated to advancing the development of autonomous vehicles through cutting-edge simulation technologies and novel iterative development processes. 

The  Software Validation  team focuses on unlocking software launches and continuous release decisions via simulation-led verification and validation strategies, prototypes, and protocols. Our collaborative environment fosters innovation and excellence, allowing us to push the boundaries of what is possible in autonomous vehicle testing. 

What You’ll Do 

As a Staff ML Validation Research Engineer, you will: 

• Lead ML-centric validation strategy  for deep learning components across perception, prediction, and planning in AV, defining evaluation methodologies with cross-functional partners. 

• Build AI validation critics and agentic acceleration workflows  using LLM- and model-based agents plus orchestration to automate scenario review, anomaly detection, and end-to-end validation flows. 

• Prototype ML research into scalable tools  by transforming ML research into performant tools integrated into CI/CD and large-scale pipelines, owning key behavior and ML validation services and data pipelines. 

• Drive simulation-based ML evaluation at scale  by evaluating deep learning modules in realistic sensor and traffic simulation and expanding behavioral and scenario coverage tightly linked to ML models. 

• Provide cross-functional collaboration and leadership  across Simulation, Safety, Systems Engineering, Autonomy, and tools teams through reviews, roadmapping, standards, and mentorship focused on high-quality, automation-first software. 

Your Skills & Abilities 

• 8+ years of experience and MS/PhD in  Computer Science, Machine Learning, Robotics, Software Engineering, Data Science , or a related field. 

• Strong proficiency in  Python  and at least one systems language (e.g.,  C++ ), with experience building production systems over large datasets. 

• Deep understanding of and experience evaluating  modern ML for robotic systems . 

• Hands-on experience using  AI agents, LLM-based tools, or workflow orchestration  to automate parts of the development, validation, or operations lifecycle. 

• Demonstrated ability to design and implement  behavioral and ML metrics  and associated tooling for validation and regression detection of complex ML systems 

• Strong analytical skills and systems thinking; able to reason about complex AV behavior and ML model interactions and turn insights into code and tools. 

• Effective communicator who can work across teams and provide technical leadership and mentorship to other engineers and researchers. 

What Will Give You a Competitive Edge 

• Background in  autonomous vehicles, vehicle development, or ADAS . 

• Demonstrated impact from introducing  automation and AI-assisted tooling  (e.g., AI agents, AI validation critics, smart monitoring) that improved scale, reliability, or engineering velocity in ML validation workflows. 

• Experience building  verification and validation tools or infrastructure  for safety-critical ML or control systems. 

• Experience working with  simulation environments and large scenario or telemetry datasets  for ML evaluation and behavior validation.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.

• ·     The salary range for this role: is $218,800 to $335,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
  ·     Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
  ·     Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include 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 and more

Información sobre diversidad

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Adecuaciones (EE.UU. y Canadá)

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