Frontier Agents Engineer (Forward Deployed Engineering)

United States Digital Space LLC; San Francisco, New York (CA, NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Scale AI is seeking a Frontier Agent Engineer (Forward Deployed Engineering) to work directly with strategic enterprise customers to architect, integrate, deploy, and operate production AI systems. You'll combine software engineering, distributed systems, cloud infrastructure, and frontier AI technologies to transform models into reliable enterprise software.

In this role you will design production agent architectures, deploy AI into mission-critical workflows, and help shape engineering best

Qualifications

• 4+ years of software engineering experience in distributed systems.
• Strong Python programming skills for production software.
• Experience with AI-powered apps using LLM APIs, agent frameworks, MCP, retrieval systems, or vector databases.
• Experience with cloud platforms (AWS, Azure, GCP) and modern production infra.
• Excellent communication and ability to work with enterprise teams.
• Strong problem-solving and ability to iterate toward production solutions.

Responsibilities

• Architect and deploy production AI systems for enterprise environments.
• Integrate with customer systems and workflows.
• Collaborate with enterprise engineering teams to deploy AI into production.
• Ensure reliability, observability, and security in production AI deployments.

Skills

Distributed systems

Python

Problem solving

Communication

Enterprise integration

AI systems engineering

Tools

Docker

Kubernetes

Infrastructure as Code

CI/CD

Job description

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems.

Every day, we work with organizations across finance, healthcare, manufacturing, media, telecommunications, and government to build production AI agents that automate complex workflows, reason over enterprise knowledge, and operate safely at scale.

The Opportunity

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 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.

What You'll BuildEnterprise AI Systems

• 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.

AI Platform Engineering

• 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.

Production AI Quality

• 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.

Customer Innovation

• 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.

Technical Leadership

• 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.

What Makes This Role Different

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

• Architecting enterprise AI systems and agent platforms

• Building integrations across cloud infrastructure and enterprise software

• Deploying AI agents into production environments

• 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.

Required Qualifications

• 4+ 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.

Preferred QualificationsEnterprise AI Engineering

• 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.

AI Systems

• 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 framewo