Senior Software Engineer, Uber AI Solutions

Uber Uber · Consumer · San Francisco, CA +1 · Engineering

Senior Software Engineer to lead platform architecture for Uber AI Solutions, focusing on building foundational model data infrastructure that integrates human expertise with ML to produce high-quality, model-ready datasets for multimodal and real-world AI use cases.

What you'd actually do

  1. Lead the design and delivery of complex backend projects, taking end-to-end ownership from requirements through production rollout.
  2. Partner cross-functionally with ML Ops, data, and product teams to translate platform and business needs into scalable, efficient backend solutions.
  3. Influence technical direction within your team, defining patterns, reviewing designs, and championing high standards for code quality and reliability.
  4. Use AI-assisted development tools and automation frameworks to improve development velocity and team productivity.
  5. Mentor engineers and support peer growth, fostering collaboration and continuous improvement within the team.

Skills

Required

  • Bachelor’s degree in Computer Science or a related field (or equivalent experience).
  • Proficient in one or more of Java, Go, Python, C++.
  • Proven track record of shipping services and working in production.
  • Demonstrated experience in system design, service reliability, scalability.
  • Experience working cross-functionally with product, design, data/ML engineering teams.
  • Excellent communication skills.

Nice to have

  • 5+ years of software engineering experience in building scalable, high-quality systems.
  • Hands-on experience designing or scaling ML Ops workflows, including feature stores, training pipelines, or model deployment systems.
  • Practical experience integrating ML-powered components into backend systems (e.g., inference APIs, model monitoring, or real-time analytics).
  • Knowledge of data labeling, collection, and translation pipelines that feed production ML models.
  • Understanding of GenAI and LLM-based systems; familiarity with prompt engineering, fine-tuning, or embedding-based retrieval frameworks.

What the JD emphasized

  • foundational model data infrastructure
  • Model Ready Datasets
  • human in the loop platform
  • data lifecycle
  • frontier research and production at scale
  • scalable platforms
  • high-quality, model-ready datasets
  • multimodal and real-world AI use cases
  • ML Ops workflows
  • feature stores
  • training pipelines
  • model deployment systems
  • integrating ML-powered components
  • inference APIs
  • model monitoring
  • real-time analytics
  • data labeling
  • collection
  • translation pipelines
  • production ML models
  • GenAI
  • LLM-based systems
  • prompt engineering
  • fine-tuning
  • embedding-based retrieval frameworks

Other signals

  • foundational model data infrastructure
  • Model Ready Datasets
  • human in the loop platform
  • data lifecycle
  • frontier research and production at scale