Staff Frontier Agents Engineer (Applied AI)
Job Overview As a Staff Frontier Agent Engineer (Applied AI) at Scale AI, you will bridge the gap between cutting-edge AI research and production deployment. You will 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. Key Responsibilities Frontier AI Systems 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. Experimentation & Evaluation 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. Production AI Engineering 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. Customer Innovation 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. What Makes This Role Different You will work across the full lifecycle of modern AI systems, from designing reasoning and agent architectures to shipping production systems into enterprise environments and measuring business impact. You will solve many different AI problems, not the same problem repeatedly, across diverse industries, datasets, model architectures, and agentic systems. Requirements 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. Compensation The base salary range for this full-time position in San Francisco, New York, Seattle is: $252,000-$315,000 USD. Benefits include comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO.