Senior Software Engineer; Backend - Ai Platform Team

About The Role

As a Senior Software Engineer in the Storage, Search, and Data (SSD) group, you will be at the heart of Uber's transition to a Cloud-Native Data Platform
. You will own business-critical systems, scaling our Distributed MySQL footprint, optimizing Hudi-based Data Lakes
, and building the storage layer. You are a “Full‑Stack Infrastructure” engineer who writes high‑performance code, designs resilient distributed systems, and ensures operational excellence for Tier‑0 services that handle millions of concurrent trips.

• Own and execute design and implementation of major features for Uber's storage and data platforms (e.g., Docstore, Pinot, or Open Search).
• Build and optimize services that leverage GCP and OCI Object Storage, focusing on high‑throughput metadata management and S3‑compatible API support.
• Drive efficiency across our HDFS and Blobstore layers, using table formats like Apache Hudi or Iceberg to improve data freshness and reduce cost.
• Work with AI teams to design high‑performance data pipelines, ensuring our storage layers can handle the intense IO demands of GPU‑based model training.
• Ensure 99.99% availability for your services, lead root‑cause analyses, improve observability, and mentor L3/L4 engineers on best practices for distributed systems.

• 5+ years of engineering experience building and maintaining large‑scale distributed systems.
• Deep storage knowledge, practical experience with:
• Relational & No
  
  SQL:
  Distributed MySQL, Cassandra, or Redis.
• Batch & Object: HDFS, S3/GCS, and metadata services.
• Distributed systems such as Google Spanner or TiDB (for transactional storage).
• Expert‑level proficiency in Java, Go, or C++ with focus on concurrency, memory management, and performance tuning.
• Experience with large‑scale analytical engines like Presto, Hive, or Trino.

• Experience with Apache Hudi, Iceberg, or Delta Lake optimizing big data storage.
• Deep familiarity with OCI or GCP and resource‑efficiency strategies.
• Understanding of how data storage interacts with ML frameworks such as Ray or PyTorch.
• Active participation in community projects like Apache Pinot, Kafka, or Flink.
• Ability to apply research‑level concepts (e.g., from CMU, Berkeley, MIT) to solve real‑world distributed consensus or indexing challenges.

For San Francisco, CA;
Seattle, WA;
Sunnyvale, CA, and all US locations, the base salary range is USD $202,000 to USD $224,000 per year. All full‑time employees are eligible to participate in a 401(k) plan, a bonus program, and may be offered equity awards and other compensation types. You will also be eligible for various benefits. More details can be found at