About the job
Working at Uber as a Graduate PhD Software Engineer II means taking deep technical expertise in AI, machine learning, and robotics and applying it to high-stakes, real-world autonomous systems. This is not a theoretical exercise; you will be building and deploying production-grade ML systems that operate in complex physical environments, where safety, reliability, and performance directly shape the future of autonomous mobility.
Responsibilities
Design, build, and deploy production-grade machine learning systems for autonomous driving applications, including perception, prediction, and decision-making
Develop and apply advanced techniques in computer vision, deep learning, robotics, and sequential decision-making to handle complex, real-world driving scenarios
Translate state-of-the-art research into scalable, high-impact solutions for autonomy systems operating in dynamic urban environments
Build and optimize large-scale data pipelines for sensor data ingestion, processing, and auto-labeling to accelerate model development
Architect and improve infrastructure for high-throughput training and low-latency inference in safety-critical, real-time systems
Own your work end-to-end: from problem formulation and modeling to offline evaluation, simulation, production deployment, and continuous iteration
Identify and solve edge cases and long-tail scenarios to improve system robustness and safety
Collaborate cross-functionally with engineers across platform, infrastructure, and product teams to deliver integrated autonomy solutions
Champion engineering excellence through code quality, rigorous testing, reproducibility, and system reliability in safety-critical environments
Qualifications
Minimum
Completing or recently completed a PhD in Computer Science, Robotics, Machine Learning, Computer Vision, Electrical Engineering, or a related technical field
Preferred
Strong publication record in top-tier AI, ML, robotics, or computer vision conferences
Deep knowledge of machine learning for robotics, computer vision, or autonomous systems
Experience working with large-scale sensor data (e.g., camera, LiDAR) and building data pipelines for ML applications
Strong proficiency in Python and experience with modern ML frameworks such as PyTorch
Experience developing or deploying ML models in real-world or safety-critical systems
Familiarity with C++ and high-performance or real-time systems
Proven ability to translate research into production-grade systems
Excellent communication skills, with the ability to explain complex technical concepts to cross-functional stakeholders