Staff Software Engineer, RL Environments & Platform

Scale AI

New York (NY)

On-site

USD 252,000 - 315,000

Full time

14 days+

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Benefits offered by this job

Health, dental, vision coverage
Retirement benefits
Learning and development stipend
Generous PTO
Commuter stipend

Job summary

Scale AI is hiring a Staff Software Engineer, RL Environments, in New York. You will own the platform to build, run, verify, and deliver RL environments at scale. You’ll design sandboxed execution, packaging, and orchestration, and work on large-scale data pipelines with real applications.

You will lead across teams, write the hard parts of the system, and drive end-to-end delivery in a fast-moving research-adjacent engineering environment.

Qualifications

  • 8+ years of software engineering experience with distributed systems
  • Strong Python skills and production software shipping
  • Experience with containerization and sandboxed execution
  • Experience with high-throughput backend systems and data pipelines
  • Hands-on experience with LLMs including tool calling and eval harnesses
  • Ability to own ambiguous problems end to end and ship a system
  • Excellent written and verbal communication to align teams

Responsibilities

  • Own the technical foundation for scalable RL environments
  • Design sandboxed execution, packaging, and orchestration
  • Instrument real applications and build task suites for capability gaps
  • Lead across teams while writing the hard parts of systems

Skills

Distributed systems
Python
TypeScript/React
Go/Rust
Containerization
Sandboxed execution
LLMs
Leadership

Tools

Docker
VMs
gVisor/Firecracker
Kubernetes

Job description

Scale AI is hiring a Staff Software Engineer, RL Environments, in New York. You will own the platform to build, run, verify, and deliver RL environments at scale. You’ll design sandboxed execution, packaging, and orchestration, and work on large-scale data pipelines with real applications.

You will lead across teams, write the hard parts of the system, and drive end-to-end delivery in a fast-moving research-adjacent engineering environment.

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