Asks for

The ad says“4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems”
The ad says“Working knowledge of modern C++ and production software development”
The ad says“Experience with large-scale distributed training or cloud-based ML infrastructure”
The ad says“Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization”
The ad says“Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures”
The ad says“Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures”
The ad says“Experience with large-scale distributed training or cloud-based ML infrastructure”
The ad says“4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems”
The ad says“Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization”
The ad says“Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization”
The ad says“Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch”
The ad says“Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch”
The ad says“4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems”
The ad says“Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures”
The ad says“Strong software engineering skills with experience developing and maintaining production-quality software”
The ad says“Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization”
The ad says“Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures”

Develop state-of-the-art machine learning models for autonomous vehicle planning using rich map, perception, routing, and contextual sensor data
Design and implement model architectures for trajectory generation, behavior planning, and decision making that balance accuracy, robustness, interpretability, and runtime efficiency
Own the end-to-end machine learning lifecycle, including data curation, feature engineering, experimentation, training, evaluation, deployment, monitoring, and continuous improvement
Design rigorous offline evaluation methodologies, validation pipelines, and metrics to measure planning quality, safety, robustness, and generalization
Analyze model behavior, investigate failure cases, and improve performance through systematic error analysis and targeted experimentation
Collaborate closely with runtime, perception, prediction, mapping, and systems teams to deploy scalable machine learning solutions into production
Design validation strategies and rule-based guardrails to ensure generated trajectories are feasible, safe, and compliant with traffic rules
Stay current with advances in machine learning, robotics, and autonomous driving, translating research innovations into production systems
Ensure technical work complies with the company’s Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes

Benefits

Unlimited PTO: Unlimited paid time off on top of company holidays to recharge and enjoy as needed
Flexible working: Flexibile work arrangements and technology to help manage life’s demands at the office and remote
Health benefits: Tiered options in medical, dental, and vision insurance to fit your unique needs
Future building: Equity, 401k and FSA options for all eligible employees to help build for your future
Professional development: Company-sponsored professional development opportunities such as conferences, training, and workshops
Catered lunches: Daily catered lunches at both of our Santa Clara and Fremont offices

Preferred experience

4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems
Strong software engineering skills with experience developing and maintaining production-quality software
Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment
Experience designing datasets, experiments, validation methodologies, and metric-driven model evaluation
Experience designing validation methodologies, automated testing, and monitoring to ensure correctness, safety, and production reliability
Excellent debugging and analytical problem-solving skills, with the ability to investigate complex issues across datasets, model behavior, and production systems
Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch
Experience analyzing edge cases, tracing failures to their root cause, and improving model robustness through systematic experimentation
Excellent communication skills and experience collaborating across cross-functional engineering teams
Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures
BS, MS, or PhD in Computer Science, Robotics, Machine Learning, or a related field
Experience with planning, prediction, motion forecasting, or trajectory generation
Experience deploying machine learning models into production environments
Working knowledge of modern C++ and production software development
Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization
Experience with large-scale distributed training or cloud-based ML infrastructure
Publications or open-source contributions in machine learning, robotics, or autonomous driving
Candidates who stand out typically have one or more of the following:
Deep expertise in autonomous vehicle planning or motion prediction
Experience developing production ML systems for safety-critical applications
Experience leading technical direction for large ML projects
Strong intuition for balancing model quality, robustness, latency, and deployment constraints
Demonstrated ability to solve ambiguous, cross-functional engineering problems involving machine learning, software systems, and autonomous driving