Senior Machine Learning Engineer

NVIDIA seeks a Senior Machine Learning Engineer in Santa Clara, CA (onsite) to build the ML backbone of the DRIVE AV perception stack, focusing on LiDAR and camera perception, data pipelines, and production-grade C++ implementations, with a salary range of USD 184,000 - 356,500 per year.

Responsibilities

• Model development: design, train, and optimize cutting edge ML models for LiDAR perception such as road element detection, semantic segmentation, and tracking.
• ML workflow orchestration: own end to end pipelines including data ingestion, model training, metrics tracking, continuous performance instrumentation, and reporting.
• Productization: translate experimental models into shipped components of the NVIDIA DRIVE AV platform, delivering efficient production code in C++.
• Innovation: monitor recent advances in machine learning and apply techniques that boost platform performance.
• Cross functional collaboration: work with LiDAR and camera teams, software engineers, and project managers to convert complex ideas into reliable autonomous driving solutions.

Requirements

• A BS or MS in Computer Science, Engineering, or a related field, or equivalent experience.
• At least 6 years of industry experience applying machine learning to real world problems.
• Strong C++ and Python programming and debugging skills, with experience building for large, complex systems.
• Hands on practical experience applying ML to LiDAR and camera perception in automotive or related domains.
• Experience with deep learning frameworks such as PyTorch or TensorFlow, plus solid grounding in the mathematical foundations of ML.
• Experience building and maintaining training and metrics workflows for large scale datasets.
• Excellent communication and analytical abilities; self motivated to solve challenging problems.

Technologies

• C++
• Python
• PyTorch
• TensorFlow
• TensorRT

Benefits

• Equity
• Benefits

Ways to Stand Out

• LiDAR or Camera Perception Experience: proven track record shipping deep learning models for LiDAR/Camera in production environments.
• Advanced Model Knowledge: familiarity with modern architectures such as Transformers and their use in visual recognition tasks.
• AV Production Experience: history delivering ML features and models into production autonomous vehicle stacks or related robotics products.
• Performance Optimization: experience optimizing models for real time inference on embedded or automotive platforms, including TensorRT.