Staff Software Engineer, RL Environments
Scale AI · San Francisco, CA; New York, NY
About this role
**Staff Software Engineer, RL Environments** **About Scale AI** Scale develops reliable AI systems for the world's most important decisions. Scale Frontier Data builds the training and evaluation data that frontier labs depend on, creating systems and expert workflows that turn human expertise into signals models can learn from. **The Role** 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. You'll design the platform (sandboxed execution, environment packaging, rollout orchestration, trajectory capture, verifier frameworks) and go deep on environments themselves by instrumenting real applications and building robust graders. This is a hands-on role where you'll set technical direction across teams while still writing the hard parts. **Required Qualifications** • 8+ years of software engineering experience with strong fundamentals in distributed systems, system design, data structures, and algorithms • Strong Python skills and production software shipping track record; comfort in TypeScript/React, Go, Rust, or similar • Deep experience with containerization and sandboxed execution (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 with LLMs including agent loops, tool calling, MCP, or eval harnesses • Demonstrated ability to own ambiguous, undefined problems end-to-end and ship them • Excellent written and verbal communication; ability to align engineers, researchers, and non-technical partners **Preferred Qualifications** *RL & Post-Training:* Direct experience building RL environments, agentic benchmarks, or eval harnesses; familiarity with post-training methods (RLHF, RLAIF, RLVR, GRPO/PPO); experience designing verifiable reward signals; experience with RL training stacks *Systems & Infrastructure:* High-scale sandbox or code-execution infrastructure experience; cloud-native infrastructure (AWS/GCP/Azure); strong observability instincts; experience building internal tools for non-engineers *Ways of Working:* Research-adjacent engineering role experience; direct work with sophisticated external technical customers; prior technical leadership at staff level in fast-moving environments
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