Senior Machine Learning Engineer, Perception - Autonomous Driving

NVIDIA is a leader in AI-powered intelligent machines, and they are seeking a Senior Machine Learning Engineer to develop and productize autonomous driving solutions. The role involves designing end-to-end perception solutions, applying innovative deep learning models, and driving data-driven development to enhance the accuracy of perception systems.

Responsibilities:

• Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways
• Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks
• Develop generalizable approaches to support diverse ODDs and Country/region expansion
• Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, labeling efficiency optimization, so that value of data is maximized
• Leverage data simulation and augmentation for solving extreme scenarios
• Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness

Requirements:

• 2+ years of technical leadership demonstrating high technical and organizational complexity
• 10+ years of hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch)
• Experience in data-driven development and collaboration with data and ground truth teams
• Strong programming skills in python and/or C++
• Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other
• MS/PhD in CS, EE, sciences or related fields (or equivalent experience)
• Proven expertise in developing generalizable perception solutions for autonomous driving or robotics using deep learning with cameras
• Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications
• Proven expertise in deep learning backed up by technical publications in leading conferences/journals
• Expertise with Transformers, BEV architectures, and modern static-world perception techniques
• Experience in working on similar online mapping and complex road detection problems is a big plus