Job Details

Job Description

Role Overview

The Senior ML Validation Research Engineer will lead applied machine learning research focused on improving verification and validation of ML components used in robotics and autonomous driving systems. This role centers on simulation-based evaluation, uncertainty modeling, scenario coverage automation, and transforming advanced ML research into working prototypes that enhance the efficiency, accuracy, and coverage of ML system validation.

Key Responsibilities

• Prototype research concepts into performant tools integrated into CI/CD and large-scale validation pipelines.

• Advance ML research for open and and closed loop simulation validation.

• Develop scenario generation, coverage-guided testing, and rare-event discovery tooling.

• Create robust metrics, predictors, uncertainty and Out-of-Distribution detection methods for autonomy ML systems.

• Evaluate deep learning modules across perception, prediction, and planning in realistic sensor and traffic simulation.

• Improve behavioral coverage and hazard-aligned metrics used in release readiness decision making.

• Collaborate with Simulation, Safety, Systems Engineering, and cross-functional partners.

• Author technical documentation, white papers, and contribute to validation methodology standards.

Research Focus Areas

• Scenario synthesis (diffusion models, generative models, counterfactuals)

• Coverage-based and fuzzing-based evaluation for autonomy behavior

• Uncertainty estimation, calibration, conformal prediction, OOD detection

• Robustness testing and perturbation frameworks

• Test suite prioritization, failure mining, and regression analysis

Minimum Qualifications

• MS + 5 years, or PhD + 3 years in ML, Robotics, Computer Science, or work related experience .

• Experience with simulation-driven ML evaluation for robotics/autonomy.

• Strong proficiency in Python, PyTorch/JAX/TensorFlow.

• Demonstrated ability to translate complex ML research ideas into functional prototypes.

• Experience integrating ML evaluation into CI/CD pipelines.

• Proven research impact through published work, internal tools, or patents.

• Strong communication skills and ability to collaborate cross-functionally.

Preferred Qualifications

• Experience with autonomy stacks (perception/prediction/planning).

• Familiarity with CARLA, SVL, DriveSim, Applied Intuition, or equivalent simulation platforms.

• Knowledge of Bayesian ML, causal inference, and sequential testing.

• Experience with digital twin systems and sensor simulation.

• Understanding of automotive safety standards (ISO 26262, UL 4600, SOTIF).

• Experience building validation dashboards and scorecards connected to release criteria.

Success Criteria

• Faster detection of ML regressions with improved test efficiency.

• Improved uncertainty and robustness metrics that support release decisions.

• Prototype tools integrated into production validation workflows.

• Tangible contributions to simulation strategy, hazard coverage, and ML confidence scoring.

Compensation:

• The expected base compensation for this role is:
  
  $170,600-$261,300
  
  Actual base compensation within the identified range will vary based on factors relevant to the position.
  
• You also need to include general information about potential commissions, if applicable.
  
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
  

Benefits:

• 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.
  

This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week .This job may be eligible for relocation benefits.
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