Job Description

Role Overview

The Senior Manager, Applied ML/AI Validation will lead a small team of applied ML/AI validation scientists focused on bringing cutting-edge AI/ML research into our software validation workflows and tools, with a primary emphasis on simulation-based validation for automated driving software.

This leader will be a hands-on individual contributor and small team manager who both sets technical direction and directly contributes to models, methods, metrics, and tools. They will help shape new AI/ML validation research projects and convert them into actionable, pragmatic solutions that improve the efficiency, insight, and trust of our validation processes.

Key Responsibilities

• Lead and grow a small team of applied ML/AI validation scientists, providing technical mentorship, career development, and guidance on priorities and execution.

• Act as a hands-on technical contributor: design, implement, and evaluate ML/AI methods, metrics, and tools for validating complex AV/ADAS software systems, with strong emphasis on simulation workflows.

• Translate AI/ML research into practice by partnering with research, software validation, and tooling teams to identify high-impact ideas and drive them through experimentation, prototyping, and integration into production validation workflows.

• Define and refine validation metrics (e.g., correlation, confidence, coverage, and simulation trust metrics) that quantify how well simulation and other validation assets represent real-world performance.

• Develop and deploy ML/AI models that enhance validation, including but not limited to:

  • Scenario selection and prioritization

  • Surrogate models and meta-modeling for simulation

  • Anomaly, regression, and outlier detection in validation data

  • Automated insight generation for validation outcomes

• Collaborate cross-functionally with software engineering, systems engineering, data engineering, safety, operations, and program leadership to align validation AI/ML work with product roadmaps and safety goals.

• Own the roadmap for applied ML/AI validation, balancing near-term pragmatic gains with longer-term research and capability-building.

• Establish best practices for experimentation, statistical analysis, model validation, and reproducibility in the context of AV/ADAS validation.

• Communicate technical results and strategy clearly to both technical and non-technical stakeholders, including senior leaders.

• Champion responsible and trustworthy AI within the validation framework, ensuring methods and tools support explainability, robustness, and safety objectives.

Required Qualifications

• 15+ years of industry experience in machine learning, applied AI, data science, or related fields, with significant experience applying ML/AI to complex, real-world systems.

• Proven experience leading technical teams (people management and/or strong tech leadership roles) in ML/AI, data science, or applied research, ideally including PhD- or MS-level scientists/engineers.

• Strong background in ML/AI methods, best practices and statistics, including experiment design, model evaluation, and metric design.

• Programming skills in Python and common ML/AI ecosystems (e.g., PyTorch, TensorFlow, JAX, Scikit-learn), including experience leading teams building production-quality analysis or tooling.

• Demonstrated experience bridging research and production: taking advanced ML/AI concepts from ideation through prototyping to integration with production pipelines, tools, or workflows.

• Hands-on experience with validation, testing, or evaluation of complex software-based or cyber-physical systems (e.g., autonomous vehicles, robotics, aerospace, large-scale distributed systems).

• Experience working with large-scale data and simulation or synthetic data environments, including designing metrics and methodologies to understand correlation and trust between simulated and real-world behavior.

• Strong communication skills, with a track record of influencing cross-functional teams and senior stakeholders through clear problem framing, data-driven insights, and technical depth.

• Bachelor’s degree in Computer Science, Electrical/Computer Engineering, Mathematics, Statistics, or a related field; or equivalent practical experience.

Preferred Qualifications

• MS or PhD in Computer Science, Electrical/Computer Engineering, Robotics, Applied Mathematics, Statistics, or related quantitative discipline.

• Direct experience with autonomous driving, ADAS, robotics, or other safety-critical domains, especially in the context of simulation-based validation and scenario-based testing.

• Experience defining and deploying correlation, confidence, and trust metrics between simulation, test assets, and real-world performance.

• Familiarity with:

  • Scenario generation and selection for simulation

  • Uncertainty estimation, calibration, and robust modeling

  • Causal inference or counterfactual analysis in complex systems

• Experience building or integrating internal validation tools and platforms, working with data engineering and infrastructure teams.

• Demonstrated ability to set technical vision and prioritize a portfolio of ML/AI projects aligned to product milestones and safety/validation goals.

• Experience operating in highly cross-functional environments with systems, safety, validation, and software engineering teams.

What You Bring

• A passion for using AI/ML to make complex safety-critical systems more trustworthy and efficient to validate, especially via simulation.

• A strong bias toward pragmatic, deployable solutions—translating cutting-edge research into tools and workflows that materially improve validation outcomes.

• A leadership style that is hands-on, collaborative, and mentoring, enabling a high-performing team while still contributing deeply at the technical level.

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 $296,300 to $453,900. 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

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