Frontier Agents Engineer (Applied AI)

Scale AI•San Francisco, NY

7d•$180,000 - $225,000•Hybrid

About The Position

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 and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Frontier Agent Engineer (Applied AI), you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in.

Requirements

4+ years of software engineering, machine learning, or applied AI experience.
Strong Python programming skills.
Experience building production AI systems using LLMs.
Experience with modern AI tooling, including OpenAI, Claude, MCP, agent frameworks, vector databases, or retrieval systems.
Strong understanding of machine learning fundamentals and modern language models.
Experience designing or evaluating AI systems using quantitative metrics.
Excellent communication skills and the ability to work directly with enterprise customers.
Experience building distributed production systems.
Experience with cloud platforms such as AWS, Azure, or GCP.
Experience with Docker, Kubernetes, CI/CD, and production observability.
Experience integrating AI systems into enterprise software environments.
Experience working directly with enterprise customers.
Ability to translate ambiguous business problems into technical architectures.
Strong written and verbal communication skills.
Experience leading technical workshops, architecture reviews, or customer design sessions.

Nice To Haves

Applied AI Experience building production AI agents or autonomous systems.
Deep understanding of reasoning, retrieval, memory, planning, and tool use.
Experience designing evaluation frameworks for LLMs and agentic systems.
Experience with RAG, semantic search, knowledge graphs, customer intelligence systems, or structured knowledge representations.
Experience with fine-tuning, distillation, reinforcement learning, small language models, or model optimization.
Familiarity with multimodal AI systems and frontier foundation models.

Responsibilities

Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use.
Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data, and deterministic software into reliable production workflows.
Engineer customer intelligence layers, retrieval pipelines, memory systems, and knowledge representations that allow agents to reason over large, heterogeneous enterprise data.
Develop multi-agent systems that coordinate reasoning, planning, tool execution, and human oversight.
Translate frontier AI research into production systems by rapidly evaluating new models, prompting techniques, reasoning paradigms, and agent architectures.
Own the full experimentation lifecycle, from hypothesis generation to production rollout.
Design rigorous evaluation frameworks using offline benchmarks, online A/B experiments, golden datasets, regression suites, LLM-as-a-Judge, and human evaluation.
Run controlled experiments and ablation studies to understand the contribution of different models, prompts, retrieval strategies, reasoning techniques, memory systems, and agent architectures.
Continuously evaluate newly released frontier models and determine where they meaningfully improve quality, latency, reliability, or cost.
Develop confidence estimation, reflection, and continuous learning systems that improve agents over time using real-world feedback.
Measure success through business outcomes, not benchmark scores.
Build production-quality AI systems with a strong emphasis on reliability, observability, latency, safety, and cost.
Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines that enable safe deployment in high-stakes environments.
Collaborate with infrastructure engineers to deploy AI systems securely within enterprise cloud environments.
Build human-in-the-loop workflows that effectively combine AI automation with expert oversight.
Partner directly with enterprise customers to understand their business, data, and operational challenges.
Translate ambiguous customer problems into production AI architectures.
Rapidly prototype new ideas, validate them with customers, and evolve successful solutions into scalable production systems.
Identify reusable patterns that become core capabilities across many enterprise deployments.
Work across the full lifecycle of modern AI systems: Designing reasoning and agent architectures, Building retrieval, memory, and customer intelligence systems, Developing predictive models that work alongside LLMs, Running experiments and ablation studies, Shipping production systems into enterprise environments, Measuring business impact through online experimentation, Continuously improving agents using real-world feedback.
Work across diverse industries, datasets, model architectures, and agentic systems, rapidly developing intuition for what makes AI systems successful in production.
Gain hands-on experience integrating AI systems into enterprise environments, deploying production infrastructure, and working directly with customer engineering teams.