Job Description
The ML Validation Research Engineer - Early Career will contribute to 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, performance monitoring and issue observability, 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.
- Develop AI-tools to improve performance monitoring and observability for autonomous vehicle stack
- Advance ML research for open and and closed loop simulation validation.
- Develop AI-centric automations to make issue triage and root cause analysis more scalable
- 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
- B.Sc or MS in ML, Robotics, Computer Science, or work related experience
- 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
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