We are looking for a Machine Learning Engineer to join our Autonomous Vehicle Perception team. In this role, you will help build perception modules that enable static world understanding without HD Map— including road layouts, lane structures, boundaries, crosswalks, and other traffic components critical for driving without reliance on HD maps. Your models must scale across continents, adapt to diverse road systems, and handle corner case scenarios towards meeting the highest standards of safety and reliability.
What You’ll Be Doing:
Teamwork: Collaborate with researchers and engineers to transform innovative algorithms into production-ready perception modules.
Exploration: Research and prototype deep learning methods for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks. Focus on scalability and generalization across geographies and driving conditions.
Experimentation: Develop and implement experiments at scale, applying detailed analytics to validate adaptability and improve generalization to rare and corner-case scenarios.
Deployment: Drive end-to-end deployment of perception models — from prototyping and validation to integration, optimization, and delivery into the autonomous driving stack.
What We Need to See:
MS or PhD in Computer Science, Engineering, or related field (focus on Deep Learning, AI, or similar), or equivalent experience.
8+ years of experience applying ML/DL to real-world perception problems.
Strong Python programming skills with proven software engineering practices.
Hands-on experience with deep neural network training, inference, and optimization using PyTorch, TensorRT (or similar).
Solid understanding of the mathematical foundations of ML/DL.
Proven experience in data-driven analysis: setting up metrics, running large-scale experiments, interpreting results, and applying insights to guide model improvements.
Experience developing scalable software for large, sophisticated systems.
Excellent interpersonal skills; able to collaborate optimally across teams.
Self-motivated, analytical, and eager to solve meaningful and challenging perception problems.
Ways to Stand Out from the Crowd:
Global Generalization: Proficiency in crafting perception systems that extend globally and adapt to different traffic environments.
Data-Centric Mentality: Strong background in data analytics, error analysis, and metric-driven iteration for ML systems.
Corner Case Mastery: Proven track record to address rare and long-tail scenarios, from unusual road markings to sophisticated intersections.
Static World Understanding Expertise: Hands-on work with lane detection, road boundaries, crosswalks, and similar tasks.
Advanced Model Knowledge: Expertise with Transformers, BEV architectures, and modern static-world perception techniques.
Intelligent machines powered by AI are no longer science fiction. GPU Deep Learning has made it possible for self-driving cars to learn, perceive, and reason about the world. NVIDIA GPUs power the algorithms that enable both static world understanding and scalable perception across global road systems. Join us and help define the future of reliable, data-driven autonomous driving.
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.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
