Senior Machine Learning Engineer, Perception - Autonomous Driving

NVIDIA; Santa Clara (CA); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA invites leading researchers and engineers to advance autonomous driving through ML and perception systems. This role focuses on designing and optimizing ML models for LiDAR and camera inputs, coordinating end-to-end ML workflows, and delivering production-ready code on the NVIDIA DRIVE AV platform.

Ideal candidates have deep ML expertise, strong C++/Python skills, and a track record in multi-sensor fusion for automotive applications.

Qualifications

• MS or PhD in Computer Science, Engineering, or a related field, or equivalent experience.
• 8+ years of relevant proven industry experience applying machine learning to address real-world problems.
• Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.
• Deep practical experience applying ML to lidar/camera perception and multi-sensor fusion in automotive or related fields.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of ML foundations.
• Building and sustaining training and essential metric workflows for large-scale datasets.
• Excellent communication and analytical skills. Self-motivated drive to solve hard problems.

Responsibilities

• Model Development: Design and optimize ML models for LiDAR/camera perception and fusion.
• Develop ML workflows: data pipelines, training, metrics, performance instrumentation, reporting.
• Productization: port ML models to production on NVIDIA DRIVE AV platform with efficient C++ code.
• Innovation: track ML advances and apply techniques to improve platform performance.
• Collaborate with LiDAR/camera teams to turn ideas into reliable autonomous solutions.

Skills

C++ programming

Python programming

Machine learning

Deep learning

Multi-sensor fusion

Lidar/Camera perception

Data pipelines

Communication skills

Education

MS or PhD in CS/Engineering or related field

Tools

PyTorch

TensorFlow

TensorRT

Job description

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and interact with people are no longer science fiction. Today, a self-driving vehicle powered by AI can meander through a country road at night and find its way. NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots, and self-driving vehicles that can perceive and understand the world.

What You’ll Be Doing

• Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception and multi-sensor fusion (e.g., object detection/classification, image classification, semantic segmentation, tracking).
• Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.
• Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to product level on the NVIDIA DRIVE AV platform, developing highly efficient product code in C++.
• Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.
• Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

What We Need To See

• MS or PhD in Computer Science, Engineering, or a related field, or equivalent experience.
• 8+ years of relevant proven industry experience applying machine learning to address real-world problems.
• Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.
• Deep practical experience applying machine learning to lidar/camera perception and multi-sensor fusion in automotive or related fields.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of the mathematical foundations of ML.
• Building and sustaining training and essential metric workflows for large-scale datasets.
• Excellent communication and analytical skills. Self-motivated drive to solve hard problems.

Ways To Stand Out From The Crowd

• Multi-sensor fusion, LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera and Multi-Sensor Fusion in a production environment.
• Advanced Model Knowledge: Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.
• AV Production Experience: A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.
• Performance Optimization: Experience with model optimization for real-time inference on embedded or automotive platforms (e.g., using TensorRT).

We believe that realizing self-driving vehicles will be a defining contribution of our generation (e.g., traffic accidents are responsible for ~1.25 million deaths per year worldwide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world’s most desirable employers with some of the most brilliant and talented people in the world working here. If you’re creative and autonomous, we want to hear from you!

#AutonomousVehicles

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 10, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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