Hiring: Staff Software Engineer, RL Environments Company: Scale AI Location: San Francisco Bay Area Job Posted Time: 2026-09-09 18:01:43 Target Skills & Keywords : Data Pipeline, Docker, Go, Kubernetes, LLM, Python, React, Reinforcement Learning, Rust, TypeScript About the job Experience: •8+ years of software engineering experience with strong fundamentals in distributed systems, system design, data structures, and algorithms. Required Skills: •As a Staff Software Engineer, RL Environments, you'll own the technical foundation for how Scale builds, runs, verifies, and delivers RL environments at scale. •An RL environment is a real piece of software: a containerized world with real dependencies, real state, real tools, and a grader that has to be correct even when the agent is creative about breaking it. Building one is a full-stack engineering problem. Building thousands of them reproducibly, cheaply, with trustworthy reward signals and throughput measured in millions of rollouts is a systems problem that very few people have solved. •You'll work on both. You'll design the platform: sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and the authoring surfaces that let engineers and domain experts produce environments without reinventing infrastructure each time. And you'll go deep on the environments themselves by instrumenting real applications, designing task suites that expose specific capability gaps, and building graders that hold up under adversarial optimization. •This is a hands-on engineering role. You'll set technical direction across multiple teams, and you'll still be the person who writes the hard part. Qualifications: •8+ years of software engineering experience with strong fundamentals in distributed systems, system design, data structures, and algorithms. •Strong Python skills and a track record of shipping production software; comfort in at least one other part of the stack (TypeScript/React, Go, Rust, or similar). •Deep experience with containerization and sandboxed execution, including Docker, VMs, gVisor/Firecracker, Kubernetes, or equivalent. •Experience building or operating high-throughput backend systems: orchestration, job scheduling, queuing, and large-scale data pipelines. •Hands-on experience building with LLMs including agent loops, tool calling, MCP, or eval harnesses, and enough intuition about model behavior to reason about what a training signal actually teaches. •Demonstrated ability to own ambiguous, undefined problems end to end and drive them to a shipped system. •Excellent written and verbal communication; ability to align engineers, researchers, and non-engineering partners on a technical direction. •Preferred Qualifications •RL & Post-Training •Direct experience building RL environments, agentic benchmarks, or eval harnesses (SWE-bench-style task suites, terminal or browser environments, tool-use benchmarks, or in-house equivalents). •Familiarity with post-training method Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!