Descripción

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

Required 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 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 $144,700- $261,300. 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:

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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