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Scale AI is seeking a Staff Frontier Agents Engineer to work at the intersection of AI and enterprise engineering. You'll partner directly with strategic enterprise customers to architect, integrate, deploy, and operate production AI systems that solve real business problems across finance, healthcare, manufacturing, media, telecommunications, and government.
In this forward-deployed engineering role, you'll combine modern software engineering, distributed systems, cloud infrastructure, and frontier AI technologies to transform cutting-edge models into reliable enterprise software. Unlike traditional infrastructure roles, you'll work across the full lifecycle of production AI systems—from initial architecture through deployment and ongoing operations.
Key responsibilities include:
**Enterprise AI Systems**: Architect and deploy production AI systems that integrate seamlessly into complex enterprise environments including cloud platforms, data warehouses, internal APIs, and proprietary software. Design scalable agent architectures combining LLMs, retrieval, memory, tools, and enterprise data. Build robust integrations allowing AI agents to safely interact with customer systems while meeting security, governance, and compliance requirements.
**AI Platform Engineering**: Develop the production infrastructure enabling frontier AI research to become reliable enterprise software. Build agent runtimes, orchestration frameworks, context pipelines, and tool integrations. Engineer systems for reliability, observability, latency, scalability, and graceful degradation. Design human-in-the-loop workflows combining AI automation with expert oversight.
**Production AI Quality**: Operationalize AI quality systems ensuring production agents remain reliable as models and data evolve. Deploy evaluation harnesses using benchmarks, experiments, golden datasets, and LLM-as-a-Judge. Implement tracing, observability, monitoring, guardrails, and safety mechanisms. Partner with Applied AI engineers to productionize new evaluation methodologies and emerging capabilities.
**Customer Innovation**: Partner directly with enterprise customers to understand their technical infrastructure and workflows. Translate ambiguous problems into scalable production AI architectures. Collaborate with customer teams to deploy AI into mission-critical workflows. Identify reusable engineering patterns across deployments.
**Technical Leadership**: Serve as primary technical advisor for strategic accounts. Lead architecture discussions spanning distributed systems, AI infrastructure, and enterprise integration. Document reusable patterns and best practices. Work with product, infrastructure, and Applied AI teams to improve the platform.
