Staff Software Engineer, RL Environments
Scale AI · San Francisco, CA; New York, NY
Posted 14 days ago
or apply directly on Scale AI's site. We never take the application ourselves.
Is this posting real?
- This role has been open
- 14 days Scale AI's roles stay open a median of 45 days
- Reposted
- No
- Salary listed
- No 2% of Scale AI's roles list one
- Ghost-job risk at Scale AI
- low 0 stale, 5 reposted of 217 open
- Hiring momentum
- 270 roles opened in the last 90 days ↑ up vs. the prior 90 days
- Last confirmed on the employer's board
- 2026-09-17
Measured from postings appearing on and disappearing from Scale AI's own greenhouse board since 2026-08-03. Full hiring picture for Scale AI.
About this role
As a Staff Software Engineer focused on RL Environments at Scale AI, you will be responsible for creating and managing the technical foundation for reinforcement learning environments. This involves designing platforms for execution, packaging, and orchestration while also developing the environments themselves, ensuring they are robust and capable of handling complex tasks. The role requires both hands-on engineering and leadership across teams, emphasizing the delivery of high-quality, scalable systems.
- benefits
- 3/5
- freshness
- 4/5
- career value
- 5/5
- role clarity
- 5/5
- pay transparency
- 0/5
Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of Scale AI as an employer.
What you need
- 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
Nice to have
- 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 methods: RLHF, RLAIF, RLVR, GRPO/PPO-family algorithms, rejection sampling, reward modeling, and the practical failure modes of each
- Experience designing verifiable reward signals, and firsthand experience with reward hacking and how to defend against it
- Experience with RL training or serving stacks (verl, TRL, Ray, vLLM, SGLang, or similar)
- Experience with high-scale sandbox or code-execution infrastructure, remote development environments, or CI systems
What you get
- Comprehensive health, dental and vision coverage
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Potential for commuter stipend
- Equity-based compensation subject to Board of Director approval
Worth weighing
- No specific information on team size or structure provided
- Role involves both technical and leadership responsibilities, which may require balancing multiple priorities
- High level of experience required may limit candidate pool
- Salary range provided, but actual compensation may vary based on multiple factors
Summarised from Scale AI's posting. Read the full original.
Listed by Scale AI on their greenhouse job board, last confirmed open on 2026-09-17. PitchMeAI is not the employer.
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