Senior Software Engineer - Data Storage

Uber Uber · Consumer · Seattle, WA +1 · Engineering

Uber's Data Storage team is seeking a Senior Software Engineer to work on its large-scale data lake, serving batch analytics, streaming processing, and AI/ML workloads. The role involves learning big data infrastructure, fine-tuning systems for performance and reliability, innovating cloud usage, and leveraging Uber's Agents platform. Candidates should have strong engineering skills in distributed systems or machine learning systems, with experience in languages like Python, Rust, Go, or Java. Experience with AI/ML frameworks and large-scale AI infrastructure is preferred.

What you'd actually do

  1. Learn the internals of big data infrastructure at Uber scale.
  2. Deep dive into specific technologies, understanding related source code and configuration. Fine-tune these systems to improve overall performance and reliability
  3. Find and build solutions to innovate the Cloud usage, striking the balance of performance and efficiency
  4. Leverage Uber’s Agents platform to build solution to better serve customers
  5. Work independently or as a team player with other team members to solve complex technical problems.

Skills

Required

  • Bachelor's degree in Computer Science or related field.
  • 5+ years of industry engineering experience.
  • Strong engineering skills, including reading open source code, implementing solutions and performance tuning.
  • Bias for action. Execute fast.
  • Experience in distributed systems or machine learning systems, programming languages such as Python, Rust, Go and Java

Nice to have

  • MS / PhD in Computer Science or related field.
  • Experience with AI/ML or big data frameworks (e.g. Pytorch, Ray, Iceberg, Lance, Gravitino, Polaris)
  • Experience developing with solutions leveraging Cloud Object Store like S3/GCS/Azure/OCI.
  • Experience developing with supporting business critical systems with 99.9+ availability
  • Experience with modern large scale of AI infrastructure

What the JD emphasized

  • performance and reliability
  • performance and efficiency
  • complex technical problems