# Staff Software Engineer, RL Environments

**Company:** [Scale AI](https://hotfix.jobs/companies/scale-ai)
**Location:** San Francisco, CA, New York, NY
**Role:** ML Engineering
**Salary:** $252k – $315k/yr
**Experience:** 8+ years
**Skills:** Python, Distributed Systems, Docker, Kubernetes, Gvisor, Firecracker, Virtual Machines, TypeScript, React, Go, Rust, Reinforcement Learning, LLMs, Ray, vLLM
**Posted:** 2026-09-02

> Staff engineer responsible for designing and scaling the infrastructure, execution environments, verifiers, and tooling used to train and evaluate AI agents. Requires 8+ years of software engineering experience, strong Python and distributed-systems expertise, and familiarity with sandboxing, high-throughput systems, and LLM workflows.

## Job Description

## Responsibilities
- Own the technical foundation for building, running, verifying, and delivering reinforcement learning environments at scale.
- Design platforms for sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and authoring tools.
- Instrument real applications and design task suites that expose specific capability gaps.
- Build robust graders and verifiable reward signals that withstand adversarial optimization and reward hacking.
- Set technical direction across multiple teams while remaining hands-on and writing complex production code.

## Requirements
- 8+ years of software engineering experience, with strong fundamentals in distributed systems, system design, data structures, and algorithms.
- Strong Python skills and production software experience, plus familiarity with another part of the stack such as TypeScript/React, Go, or Rust.
- Deep experience with containerization and sandboxed execution, including Docker, virtual machines, gVisor, Firecracker, Kubernetes, or equivalent technologies.
- Experience building or operating high-throughput backend systems involving orchestration, job scheduling, queuing, and large-scale data pipelines.
- Hands-on experience with LLMs, including agent loops, tool calling, MCP, or evaluation harnesses.
- Ability to own ambiguous problems end to end and deliver shipped systems.
- Excellent written and verbal communication, with the ability to align engineers, researchers, and non-engineering partners.

## Nice-to-Haves
- Experience building reinforcement learning environments, agentic benchmarks, or evaluation harnesses, including SWE-bench-style task suites, terminal or browser environments, or tool-use benchmarks.
- Familiarity with RLHF, RLAIF, RLVR, GRPO/PPO-family algorithms, rejection sampling, reward modeling, and their practical failure modes.
- Experience designing verifiable reward signals and defending against reward hacking.
- Experience with RL training or serving stacks such as verl, TRL, Ray, vLLM, or SGLang.
- Experience with high-scale sandbox or code-execution infrastructure, remote development environments, or CI systems.
- Experience with AWS, Google Cloud, Azure, Infrastructure as Code, and CI/CD.
- Strong observability practices, including tracing, structured logging, and metrics.
- Experience building internal tools and data-dense review or annotation interfaces.
- Experience translating research goals into production systems, working with sophisticated technical customers, and providing staff-level technical leadership.

## Compensation
- Base salary range: $252,000–$315,000 USD.
- Eligible roles may include equity and benefits such as health, dental and vision coverage, retirement benefits, learning and development support, generous paid time off, and potentially a commuter stipend.

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