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