2 months ago
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 AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.