Senior Frontier Agents Engineer (Forward Deployed Engineering)

The hardest part of enterprise AI isn't building a great model-it's building AI systems that reliably operate inside complex production environments.

As a Senior Frontier Agent Engineer (Forward Deployed Engineering) , you'll work directly with strategic enterprise customers to architect, integrate, deploy, and operate production AI systems. 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 or platform roles, you'll work at the intersection of AI and enterprise engineering. You'll design production agent architectures, integrate with customer systems, deploy AI into mission-critical workflows, and help shape the engineering best practices for the next generation of enterprise AI.

If you enjoy solving difficult systems problems, rapidly prototyping new AI capabilities, and bringing frontier AI into production, you'll fit right in.

- Architect and deploy production AI systems that integrate seamlessly into complex enterprise environments, including cloud platforms, data warehouses, internal APIs, business applications, and proprietary software systems.

- Design scalable agent architectures that combine LLMs, retrieval, memory, tools, structured knowledge, and enterprise data into reliable production workflows.

- Build robust integrations that allow AI agents to safely interact with customer systems while meeting enterprise requirements for security, governance, and compliance.

- Rapidly prototype new AI capabilities and evolve successful prototypes into production-ready systems.

- Build the production infrastructure that enables frontier AI research to become reliable enterprise software.

- Develop agent runtimes, orchestration frameworks, context pipelines, execution services, and tool integrations that power production AI systems.

- Engineer systems for reliability, observability, latency, scalability, retries, fallback strategies, and graceful degradation.

- Design human-in-the-loop workflows that effectively combine AI automation with expert oversight.

- Build deployment patterns that allow AI systems to evolve safely through continuous delivery and experimentation.

- Operationalize modern AI quality systems that ensure production agents remain reliable as models, prompts, and customer data evolve.

- Deploy evaluation harnesses using offline benchmarks, online experiments, golden datasets, regression suites, and LLM-as-a-Judge to detect quality regressions before they impact customers.

- Implement tracing, observability, monitoring, guardrails, grounding, and safety mechanisms that enable production AI systems to operate with confidence.

- Partner closely with Applied AI engineers to productionize new evaluation methodologies, retrieval strategies, reasoning architectures, and emerging AI capabilities.

- Rapidly evaluate newly released models, agent frameworks, evaluation methodologies, and developer tooling, determining how they can be safely adopted into production systems.

- Partner directly with enterprise customers to understand their technical infrastructure, software architecture, and operational workflows.

- Translate ambiguous customer problems into scalable production AI architectures.

- Collaborate with customer software engineers, ML engineers, platform teams, and product organizations to deploy AI into mission-critical workflows.

- Identify reusable engineering patterns that become core capabilities across multiple enterprise deployments.

- Serve as the primary technical advisor for strategic enterprise accounts.

- Lead architecture discussions spanning distributed systems, AI infrastructure, enterprise integration, and production deployment.

- Document reusable architecture patterns, deployment strategies, integration frameworks, and operational best practices.

- Work closely with Scale's product, infrastructure, and Applied AI teams to continuously improve the platform.

You'll work across the full lifecycle of production AI systems:

- Building integrations across cloud infrastructure and enterprise software

- Operationalizing evaluation frameworks, guardrails, and observability

- Running production experiments and measuring real-world business impact

- Continuously improving deployed AI systems using customer feedback and operational telemetry

You'll work with the latest frontier AI models, evaluation methodologies, agent frameworks, and developer tooling as they emerge, helping customers adopt new AI capabilities safely and effectively.

Rather than supporting a single product or platform, you'll solve diverse engineering challenges across industries and use cases, rapidly building expertise across enterprise architecture, AI systems engineering, and production deployment.

- 5+ years of software engineering experience with strong fundamentals in distributed systems, data structures, algorithms, and system design.

- Strong Python programming skills with experience building production software.

- Experience building or deploying AI-powered applications using modern LLM APIs, agent frameworks, MCP, retrieval systems, or vector databases.

- Experience with cloud platforms (AWS, Azure, or GCP) and modern production infrastructure.

- Strong problem-solving skills with the ability to navigate ambiguous technical requirements and rapidly iterate toward production solutions.

- Excellent communication skills and the ability to work directly with enterprise engineering teams.

- Experience deploying production AI agents or autonomous systems.

- Experience designing distributed systems, APIs, orchestration services, or large-scale backend systems.

- Experience with cloud-native infrastructure, Docker, Kubernetes, Infrastructure as Code, and CI/CD.

- Experience integrating AI systems into enterprise software environments.

- Familiarity with modern agent architectures, retrieval systems, tool use, memory, and context engineering.

- Experience with evaluation frameworks, LLM observability, regression testing, tracing, and AI monitoring.

- Experience implementing guardrails, grounding, and safety mechanisms for production AI systems.

- Experience operationalizing new foundation models, agent frameworks, or AI infrastructure.

- Ability to translate complex technical requirements into scalable production systems.

- Experience leading architecture reviews, technical workshops, or customer design sessions.

While this role initially emphasizes enterprise engineering and production deployment, every Frontier Agent Engineer develops expertise across both Forward Deployed Engineering and Applied AI.

Over time, you'll deepen your understanding of modern agent architectures, evaluation methodologies, retrieval systems, reasoning techniques, and emerging AI technologies, while continuing to build world-class production software. Our goal is to develop engineers who can design, build, evaluate, deploy, and continuously improve frontier AI systems end to end.