Staff System Engineer

Our client is building the data layer for a world where AI agents, not humans, run production workloads. Today's data platforms assume a careful person at the keyboard. This one lets agents run, inspect, verify, and undo real jobs against production data, safely. They already have paying customers using it live. You'd join as one of the first engineers building this piece: real ownership over how it gets built, not just a seat on someone else's roadmap. Specifically, you'd own the Runner, the layer that executes compute at scale in the cloud. Python is the main language, backed by a growing amount of Rust. This is a hands‑on tech lead role. You make the calls on your area and stay in the code full time; there's no path to management here, by design. What you'd work on Systems that let people and agents safely run, check, and roll back data jobs at production scale APIs and abstractions simple enough for a human to read and solid enough for an agent to call Performant execution of Rust, Python, and SQL workloads over object storage Runtime work: scheduling, concurrency, caching, reliability Your first 90 days First 30 days: get comfortable with the architecture, codebase, tooling, and deployment process. Understand the main production workflows end to end. Ship a small feature or a meaningful improvement. By 60 days: own one area of the platform and contribute to technical decisions. Debug real production issues. Start building trust with junior team members. By 90 days: make meaningful improvements with more independence. Lead the design of a significant new capability. Become a trusted voice in technical and operational discussions. What you need 7+ years designing, building, and operating distributed systems: concurrency, async programming, fault tolerance, failure recovery Fluent in Python plus a low-level language; real Rust experience is a strong plus, but genuine willingness to go deep in Rust also works Hands‑on experience building and running cloud infrastructure Modern engineering practice: Git, containers, infrastructure as code, CI/CD, testing, security, observability Comfortable writing technical design docs, leading technical discussions, and mentoring A track record of owning ambiguous problems from first requirements through production Working knowledge of SQL and relational data systems, object storage and cloud‑native storage architecture, distributed computation (Kubernetes, Spark, query engines, or custom infrastructure), and API or developer‑tooling design. Good to have: database internals, query planning, or execution engines; Apache Arrow, DataFusion, or DuckDB; transaction processing, Apache Iceberg, or data catalogs; serverless runtimes or sandboxed execution; infrastructure for coding agents or autonomous software development. Base pay matches the market rate for the right candidate, plus 0.40-0.60% equity #J-18808-Ljbffr Sage Recruiting Inc.