Senior ML Validation Engineer

Key Responsibilities

• Prototype research concepts into performant tools integrated into CI/CD and large-scale validation pipelines.
• Advance ML research for open and closed loop simulation validation.
• Create 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.

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.