Frontier Agents Engineer (Applied AI)
San Francisco, CA$180k - $225kFull-timePosted 2mo ago
About the role
Applied AI engineer role focused on bridging cutting-edge frontier AI research with production deployment to design, evaluate, and deploy intelligent systems for enterprise customers across various use cases.
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 contributions of 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 reliability, observability, latency, safety, and cost considerations.
- Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines for 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 ideas, validate with customers, and evolve solutions into scalable production systems.
- Identify reusable patterns that become core capabilities across many enterprise deployments.
Qualifications
- 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.
Skills
Required:PythonProduction AI systemsLLMs and retrievalCommunication with enterprise customers
Benefits
- comprehensive health, dental and vision coverage
- retirement benefits
- learning and development stipend
- generous PTO
- commuter stipend (possible)
Last updated Oct 21, 2026. Interested? Apply on the company site.
About Scale AI
Data-centric infrastructure to accelerate the development of AI
Scale accelerates the development of AI within organizations of any size to deliver critical business insights and operational efficiency. Its data-centric infrastructure platform leverages RLHF (Reinforced Learning with Human Feedback) to help organizations build the strongest AI models that supercharge their business, with customers across industries including Meta, Microsoft, U.S. Army, DoD’s Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart and Flexport.
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