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.

What the Candidate Will Do

- 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.

Basic Qualifications

- 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 TensorFlow (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.

Preferred Qualifications

- 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, TensorFlow 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.