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Uber is launching AV Labs to accelerate the autonomous technology ecosystem. We're building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team is focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race—and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match. As a Principal ML Engineer, you will be at the forefront of Physical AI, building advanced autonomy algorithms and models to add rich semantics to our massive driving data. You will set the technical direction for developing state-of-the-art machine learning that enables better data mining, deep scene understanding, and causal modeling of ego vehicle behavior. You aren't just solving known problems; you are architecting the intelligence behind our L4 data and evaluation engine. You will collaborate with engineers across big data, compute, and cloud engineering to build platforms that harness scale and real-world complexity to reimagine how the world moves.
Job Responsibility
Strategic Semantic Modeling: Lead the strategy for developing autonomy algorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases to enrich our L4 data lake
Scene and Behavior Causality Understanding: Provide the overarching technical vision for multi-modal scene understanding and modeling the causality behind ego vehicle behaviors from logged data
Technical Mentorship & Influence: Mentor senior and lead engineers, fostering a culture of rigorous experimentation and engineering excellence
Cross-Organizational Leadership: Act as a bridge between AV Labs and other Uber engineering units to ensure our semantic models and data evaluation platforms are successfully integrated and deployed at scale
Requirements
10+ years of working experience in the ML, Robotics, or Autonomous Systems industry
Proven experience leading large-scale technical projects from conception to production
Bachelor's degree in Computer Science, Computer Engineering, or related fields
Expert-level proficiency in Python and Linux environments
Deep expertise in modern AI/ML frameworks (e.g., PyTorch, TensorFlow)
Nice to have
PhD degree in Robotics or Machine Learning with a focus on Autonomous Driving, Computer Vision, or foundation models
Extensive experience with C++, CUDA, and high-performance system optimization for massive offline datasets
Deep understanding of autonomous system architectures, sensor data pipelines, and offline evaluation simulation
Experience building and scaling Foundation Models for physical world interaction, scene representation, or causal behavior modeling
Recognized expertise in the field (e.g., relevant patents, open-source contributions, or publications)