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.

Responsibilities

• 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.

Qualifications

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.

Pay

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 role is USD $202,000 per year - USD $224,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp.

Benefits

All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.

Schedule

Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.