9 days ago
Santa Clara, CA, USASenior / Staff+
Base Salary
$130k - $220k/yr
Responsibilities
- Develop machine learning models and architectures for autonomous vehicle planning, trajectory generation, behavior planning, and decision making.
- Own the end-to-end ML lifecycle, including data curation, feature engineering, experimentation, training, evaluation, deployment, monitoring, and continuous improvement.
- Design offline evaluation methodologies, validation pipelines, metrics, automated testing, and monitoring for planning quality, safety, robustness, and reliability.
- Analyze model behavior and failure cases through systematic error analysis and targeted experimentation.
- Collaborate with runtime, perception, prediction, mapping, and systems teams to deploy scalable production ML solutions.
- Design validation strategies and rule-based guardrails to ensure generated trajectories are feasible, safe, and compliant with traffic rules.
- Stay current with machine learning, robotics, and autonomous-driving advances and translate research innovations into production systems.
- Ensure technical work complies with the company’s Quality Management System, customer requirements, regulatory standards, and internal engineering processes.
Requirements
- Bachelor’s, master’s, or PhD in Computer Science, Robotics, Machine Learning, or a related field.
- At least 4 years of experience developing machine learning systems for robotics, autonomous driving, or real-time decision-making systems.
- Strong Python skills and experience with modern deep learning frameworks such as PyTorch.
- Understanding of deep learning, sequence modeling, transformers, diffusion models, and other modern ML architectures.
- Experience designing datasets, experiments, validation methodologies, and metric-driven model evaluation.
- Strong software engineering skills and experience developing and maintaining production-quality software.
- Experience debugging complex issues across datasets, model behavior, and production systems and improving robustness through systematic experimentation.
- Experience designing validation methodologies, automated testing, and monitoring for correctness, safety, and production reliability.
- Preferred experience includes planning, prediction, motion forecasting, trajectory generation, production ML deployment, modern C++, TensorRT, ONNX Runtime, CUDA, distributed training, cloud-based ML infrastructure, or relevant publications and open-source contributions.
- Bonus qualifications include autonomous vehicle planning or motion prediction expertise, safety-critical production ML experience, technical leadership for large ML projects, and the ability to balance model quality, robustness, latency, and deployment constraints.
Benefits
- Highly competitive salary and benefits package including a 401(k) plan, catered free lunch, unlimited snacks and beverages, and opportunities for personal and professional development.
Categories
ML EngineeringRobotics
