What if a node failure didn’t have to ripple across your cluster? M3DB’s subclustered placement algorithm creates smaller, self-contained failure domains—limiting shard migration impact, enabling safer parallel operations, and making large clusters easier to reason about. ⬇️ uber.com/us/en/blog/from-cha…

How do you keep ML features consistent at 8M QPS? We built a feature logging framework that selectively captures the features used at inference for training, reducing training-serving skew without logging trillions of unnecessary rows. See how it works. ⬇️ uber.com/us/en/blog/taming-m…

9.5M unnecessary requests. Stopped. Uber’s error ownership system identifies where failures originate so retries happen where they can actually help, not repeatedly across the call chain. The result: max retry storm radius dropped from up to 25 levels to 3. uber.com/us/en/blog/protecti…

At Uber’s scale, tracing every failure across a complex service mesh isn’t practical. So we built a system that automatically maps how failures propagate across dependencies—helping us identify hard dependencies and strengthen reliability. uber.com/us/en/blog/automate…

⚡ We cut Uber Eats search latency in half. How? Measure. Identify. Fix. Validate. Repeat. An AI coding agent helped us find bottlenecks, test fixes, and validate impact—turning optimization into a continuous feedback loop. See how → uber.com/us/en/blog/uber-eat…

Since I previously shared how we are scaling AI usage so quickly without costs rising at the same rate, there’s been a lot of interest in the engineering details. So I asked the team to open up the hood and share what we’ve learned. One thing we’ve always been very good at @Uber is understanding the economics of a system at a deep level. And I’ve always believed that constraints drive innovation. We took the opportunity to turn the cost problem into an engineering problem. We broke AI spend down into its underlying levers and started systematically attacking each one: • Scaling vendor-neutral managed agents • Building Uber SWE benchmarks to measure what actually works • Optimizing prompt caching • Making tool and MCP usage more efficient • Grounding trajectories in our context graph • Building efficient, reusable agent skills • Giving engineers real-time cost visibility and optimization tips There’s a lot more work to do in this space: scaling more autonomous agents, dynamic model routing, a self-evolving context graph, and continuously updating efficient agent skills. There's still a tremendous amount to invent here, and we’ll keep sharing what we learn as we build and deploy it at Uber. This is an incredible time to be an engineer. The economics of software are changing, the way we build is changing, and engineers have an opportunity to help define what comes next. Read the deep dive here: uber.com/us/en/blog/efficien… and let us know what you think.

Across all agentic tools and all employees at Uber, weekly active users have grown 7x and weekly agent requests have grown 9.4x. Despite that growth, total AI spend has relatively stabilized and cost per token has decreased. In our latest blog, we break down the architecture and operational choices driving AI efficiency at scale, including better prompt caching, access to 1,000+ internal and third-party MCP servers through a single gateway, and an AI Context Graph spanning 30+ internal systems. The result? Cost per 1,000 requests for a given frontier model has fallen 34% from its peak, and cost per session is down 52%. Learn more ⬇️

The recording of the @aiDotEngineer World's Fair session from Jun 30, 2026, that @hudaman and I did on Building Blocks for Uber's Software Factory is now live! We’re moving closer to an autonomous, agent-driven Software Factory at @UberEng. Underneath it is an AI infrastructure stack designed to let agents safely understand our systems, take action, write and validate code, and maintain software at Uber scale. Six building blocks power the factory: * Model Gateway: PII redaction, safety guardrails, observability supporting 800+ projects and 100M+ requests/day. * MCP Gateway: Connects agents to thousands of internal APIs and SaaS tools, with token-efficient patterns like Omni MCP, CLI, and code-mode skills. * Devpods: Pre-provisioned Kubernetes environments where agents can securely build, execute, and test code. * Skills Marketplace: Skills Marketplace: 3,6K skills with 30K+ executions/day, packaging reusable engineering workflows for agents. * Context Graph: 100M+ entries across 200+ node and edge types, helping agents discover context with lower token and latency overhead. * Cortana: Our AI assistant bringing everything together across Slack, CLI, and web, handling 25K+ sessions/day. We also demoed the full agentic SDLC, taking an idea from Figma → code generation → visual validation → self-healing CI → AI code review → automated code maintenance. 70%+ of Uber PRs originate from local or cloud agents, lines of code per engineer have doubled YoY, and 250+ automated migrations have touched 9M lines of code. Full video here: piped.video/watch?v=17-YSUHo…

A query can return few records and still scan years of data. At Uber, we reduced the cost of these “needle in a haystack” export workloads by combining Apache Hudi column stats with predicate-column sorting for selective file pruning. 🔗 Read more: uber.com/us/en/blog/running-…

Discover the core principles behind Uber's Payments Platform—designed 10 years ago and still powering a business with $217B in annualized gross bookings. 🔗uber.com/us/en/blog/ubers-pa…

Big ideas, bold builds, and incredible energy! 🚀 We loved celebrating our interns for #NationalInternDay and seeing their creativity come to life at this year’s intern hackathon. The future of engineering is looking bright! 💪

How many unique users did Uber serve this quarter? For mission-critical metrics, approximate wasn’t enough. We built a chunked aggregation buffer strategy for exact distinct counts at scale, cutting backfill time by 65% on average. Read more: uber.com/us/en/blog/scaling-…

Cloning multi-gigabyte monorepos creates an infrastructure bottleneck. GitFarm serves Git as a Service via a gRPC API, delivering checkouts under 500ms and cutting resource overhead by 80%+: uber.com/us/en/blog/gitfarm-… #UberEngandSci #UberEngineering #PlatformEngineering

Congrats to Uber engineers Gaurav Goel and Sanjeev Suresh on being named Agentic AI Foundation ambassadors! Their appointments reflect Uber’s commitment to building world-class tech and contributing to the broader developer community. #UberEngineering #AgenticAI

A look back at @aiDotEngineer World's Fair! 👋 From end-to-end agentic SDLC to AI-powered code review and production evals, the Uber Engineering team presented a taste of what we're building. Follow along for what's next.

Join Uber Engineering at @aiDotEngineer World's Fair! Hear insights directly from the engineering teams applying AI across the software development lifecycle. See full session details: ai.engineer/worldsfair/sched…

Learn how Uber built zone-failure-resilient OpenSearch® using isolation groups and shard allocation awareness to keep search and ingestion running through zone outages. 🔗uber.com/us/en/blog/zone-fai… #UberEngineering #UberEngAndScience