Staff Frontier Agents Engineer, Scale AI

• 8+ 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

Desirable

• 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 Staff 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
• 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
• You'll 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