Base Salary
$252k - $315k/yr
Responsibilities
- Own the technical foundation for building, running, verifying, and delivering reinforcement learning environments at scale.
- Design sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and environment authoring surfaces.
- Build and operate high-throughput systems for orchestration, job scheduling, queuing, and large-scale data pipelines.
- Instrument real applications, design task suites that expose capability gaps, and build graders resilient to adversarial optimization.
- Set technical direction across multiple teams while remaining hands-on with complex implementation work.
- Translate research goals into production systems and align engineers, researchers, and non-engineering partners on technical direction.
Requirements
- 8+ years of software engineering experience with strong fundamentals in distributed systems, system design, data structures, and algorithms.
- Strong Python skills and a record of shipping production software, plus familiarity with TypeScript/React, Go, Rust, or a similar technology.
- Deep experience with containerization and sandboxed execution, including Docker, virtual machines, gVisor, Firecracker, Kubernetes, or equivalent.
- 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, and an understanding of model behavior and training signals.
- Ability to own ambiguous problems end to end and drive them to shipped systems.
- Excellent written and verbal communication and the ability to align technical and non-technical partners.
- Preferred experience includes RL environments, agentic benchmarks, evaluation harnesses, post-training methods, verifiable reward signals, reward hacking defenses, and RL training or serving stacks such as verl, TRL, Ray, vLLM, or SGLang.
- Preferred experience includes high-scale sandbox or code-execution infrastructure, remote development environments, CI systems, cloud-native infrastructure across AWS, GCP, or Azure, infrastructure as code, CI/CD, observability, internal tools, research-adjacent engineering, external technical customers, and staff-level technical leadership.
Benefits
- Base salary, equity, and benefits are available for eligible roles.
- Comprehensive health, dental, and vision coverage.
- Retirement benefits, learning and development stipend, and generous paid time off.
- Potential commuter stipend and other additional benefits for eligible roles.
- Full-time position located in San Francisco, New York, or Seattle; the job posting advises referencing the subtitle for the specific location.
Categories
About Scale AI
Scale’s mission is to develop reliable AI systems for the world’s most important decisions. We provide the high-quality data and full-stack technologies that power the world’s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. The Scale Generative AI Platform allows customers to build, evaluate, and control advanced AI agents and applications that continuously improve. The Scale Data Engine provides the technology to collect, curate, and annotate high-quality datasets. Through our Scale Labs, we test models with rigorous benchmarks and novel research to ensure breakthroughs translate into systems people can trust. Scale powers the most advanced LLMs and generative models in the world through RLHF, data generation and model evaluation. We work with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.