Senior ML Engineer, Computer Vision - Applied AI

About

The Role

Applied AI at Uber builds intelligent systems that power critical product experiences across the platform. As a Senior Machine Learning Engineer - Computer Vision, you will develop and deploy state-of-the‑art vision and multimodal models that enable scalable document understanding and transcription systems across Uber's ecosystem.

Your work will power high-impact applications such as earner onboarding verification, receipt transcription, restaurant menu digitization, and other document intelligence workflows. You will design, train, and optimize modern computer vision and vision‑language models, integrating them into production systems that operate reliably at large scale.

This role combines deep model development expertise with production engineering rigor - ideal for someone who thrives at the intersection of research innovation and real-world deployment.

• Develop and train state-of-the‑art computer vision and multimodal models (e.g., Vision‑Language Models such as Gemini or similar foundation models) to transcribe and understand diverse document types including identity documents, receipts, invoices, and restaurant menus.
• Design and implement scalable vision systems, combining on‑device and server‑side models to balance latency, accuracy, privacy, and cost efficiency.
• Collaborate closely with ML Infrastructure and Earner/Product teams to define data requirements, labeling strategies, evaluation metrics, and integration pathways into the broader ML lifecycle.
• Own the full system lifecycle, from advanced model development and experimentation to production deployment, monitoring, and scaling for high‑throughput applications.
• Build and maintain robust evaluation frameworks to measure transcription accuracy, document understanding performance, and model robustness across diverse geographies and document formats.
• Optimize models for performance and efficiency, including model compression, quantization, and hardware‑aware optimization for mobile or edge deployment when required.
• Analyze production data and failure cases to continuously improve model quality, generalization, and system reliability.

• 5+ years of hands‑on experience in machine learning, with a strong focus on computer vision or multimodal systems.
• Solid foundation in deep learning fundamentals, including training, evaluation, and debugging of neural networks.
• Proficiency in Python and modern ML frameworks such as PyTorch, JAX, or Tensor Flow (Lite).
• Experience deploying ML models into production systems and working with real‑world datasets.
• Strong problem‑solving skills and ability to work cross‑functionally in product‑driven environments.

• 5+ years of hands‑on ML experience, preferably in robotics, computer vision, or embodied AI.
• Strong experience training and optimizing large‑scale vision or multimodal models, including Vision‑Language Models (VLMs) or foundation models.
• Deep understanding of computer vision techniques such as object detection, segmentation, OCR, document layout understanding, and point cloud processing.
• Experience deploying models to edge devices or mobile platforms, including performance optimization (quantization, pruning, Tensor Flow Lite, ONNX, etc.).
• Experience working with large‑scale document or visual datasets, including data curation and augmentation strategies.
• Familiarity with distributed training systems and scalable ML infrastructure.

For San Francisco, CA-based roles:
The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For Seattle, WA-based roles:
The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

For Sunnyvale, CA-based roles:
The base salary range for this…