Deep Learning Engineer, End - to - end Autonomous Driving

Autonomous Driving Engineer

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous vehicles. We are seeking highly skilled engineers to join our autonomous driving team to develop and implement end-to-end solutions for autonomous vehicle systems. This role involves designing, optimizing, and deploying state-of-the-art algorithms that enable vehicles to perceive, plan, and navigate complex environments seamlessly. The ideal candidates will have expertise in deep learning, machine learning, computer vision and software engineering, with a passion for pushing the boundaries of autonomous driving technology.

What You'll be doing:

• Design and train innovative large-scale models, including generative, imitation and reinforcement learning approaches to enhance the planning and reasoning capabilities of our driving systems.
• Explore and implement novel data generation and data collection strategies to improve the diversity and quality of the training datasets.
• Collaborate with cross-functional teams to deploy AI models in production environments, ensuring they meet stringent standards for performance, safety, and reliability.
• Integrate machine learning models directly with vehicle firmware and deliver production-quality, safety-critical software.

What we need to see:

• Deep understanding of modern deep learning architectures and optimization techniques.
• Proven track record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
• Master's degree or PhD (or equivalent experience).
• 5+ years of work experience in AV or related field.
• Strong programming skills in Python and proficiency with major deep learning frameworks.
• Familiarity with C++ for model deployment and integration with the safety-critical autonomous driving stack.

Ways to stand out from the crowd:

• Hands-on experience in large generative models' pre-training and fine-tuning.
• Deep understanding of behavior and motion planning in real-world applications.
• Experience in building and training industry-level large datasets and models.
• Proven ability to optimize algorithms for real-time performance in resource-constrained systems.
• Strong track record of taking projects from conceptualization to production deployment.