Senior Software Engineer

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.

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