Senior ML Engineer, Computer Vision for Documents

Uber; San Francisco (CA); Hybrid; USD 202,000 - 224,000; Full time

Benefits offered by this job

401(k) plan

Equity awards

Bonus program

Benefits package

Job summary

Uber is seeking a Senior Machine Learning Engineer - Computer Vision to develop and deploy state-of-the-art vision and multimodal models for scalable document understanding and transcription across the platform.

You will build robust CV/VLM systems, optimize for latency and privacy, and collaborate with cross-functional teams to deploy reliable, high-scale solutions across multiple Uber products.

Qualifications

• 5+ years hands-on experience in ML focusing on computer vision or multimodal systems.
• Strong foundation in deep learning including training, evaluation, and debugging neural networks.
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow (Lite).
• Experience deploying ML models into production with real-world datasets.
• Strong problem-solving and cross-functional collaboration in product-driven environments.

Responsibilities

• Develop and train state-of-the-art computer vision and multimodal models for document understanding and transcription.
• Design scalable vision systems balancing latency, accuracy, privacy, and cost.
• Collaborate with ML Infrastructure and Earner/Product teams on data requirements and integration pathways.
• Own end-to-end system lifecycle from development to production deployment and monitoring.
• Build robust evaluation frameworks for transcription accuracy and robustness across geographies.
• Optimize models for performance and edge/mobile deployment where needed.

Skills

Computer vision

Multimodal ML

Python

PyTorch

JAX

Tools

TensorFlow Lite

ONNX

Job description

Uber is seeking a Senior Machine Learning Engineer - Computer Vision to develop and deploy state-of-the-art vision and multimodal models for scalable document understanding and transcription across the platform.

You will build robust CV/VLM systems, optimize for latency and privacy, and collaborate with cross-functional teams to deploy reliable, high-scale solutions across multiple Uber products.