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Staff ML Engineer - Applied AI

India, Bangalore · Job Posted April 11, 2026
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Job Description

Applied AI is a horizontal AI team at Uber partnering with product and platform teams across the company to deliver cutting-edge machine learning solutions for core business problems. We specialize in areas like Generative AI, Computer Vision, Personalization, and the ML infrastructure needed to scale these systems in production. We’re looking for a Staff Engineer with deep experience in classical machine learning, generative AI, and ML systems design to help build and scale high-impact AI solutions. In this role, you’ll lead technically complex projects, influence the architecture of ML systems, and collaborate cross-functionally to drive innovation and impact across multiple Uber surfaces. This is a great opportunity for a strong technical leader who thrives in a fast-paced, product-driven environment and wants to be at the forefront of applied AI at scale.

Job Responsibility

  • Design and implement ML-driven systems that power core Uber experiences, with a focus on scalability, reliability, and performance
  • Lead the technical execution of key projects involving classical ML, deep learning, and generative AI technologies (e.g., LLMs, multimodal models)
  • Collaborate closely with product, data science, and infrastructure teams to develop AI solutions from ideation through production deployment
  • Contribute to and influence the technical direction for Applied AI, particularly around system design, model architecture, and infrastructure decisions
  • Champion engineering best practices in ML development — including experimentation workflows, model versioning, evaluation, monitoring, and responsible AI
  • Provide mentorship to engineers on the team and across partner orgs to help raise the technical bar

Requirements

  • 10+ years of industry experience in machine learning or software engineering, with a proven record of delivering ML solutions to production
  • Strong knowledge of machine learning, deep learning, and exposure to generative AI techniques (e.g., transformers, LLMs, diffusion)
  • Experience designing and scaling ML systems or platforms, including training pipelines, serving infrastructure, and model lifecycle tooling
  • Fluency in ML frameworks (e.g., PyTorch, TensorFlow, JAX) and development in Python and/or scalable backend languages (e.g., Java, Go)
  • Excellent collaboration and communication skills with the ability to work across teams and functions

Nice to have

  • PhD in Computer Science, Machine Learning, or a related field
  • Hands-on experience integrating LLMs or generative models into product experiences (e.g., summarization, personalization, automation)
  • Familiarity with MLOps, experimentation frameworks, or ML observability tools
  • Track record of technical leadership in multi-disciplinary projects involving engineering, data science, and product
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