Frontier Agents Engineer (Applied AI)
$180k - $225k • Remote • San Francisco, CA; New York, NY
Posted 5d ago
Job Location
San Francisco, CA; New York, NY
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Engineer
About the job
Scale AI is seeking a Frontier Agents Engineer (Applied AI) to bridge the gap between cutting-edge AI research and production deployment. In this role, you will partner 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, you will work across a diverse portfolio of AI challenges spanning multiple industries and use cases, building systems that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. 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.
Responsibilities
- Design and deploy production AI agents leveraging large language models, reasoning, retrieval, memory, and tool use.
- Architect intelligent systems combining LLMs, traditional machine learning, structured knowledge, enterprise data, and deterministic software.
- Engineer customer intelligence layers, retrieval pipelines, memory systems, and knowledge representations for reasoning over large, heterogeneous enterprise data.
- Develop multi-agent systems for coordinated reasoning, planning, tool execution, and human oversight.
- Translate frontier AI research into production systems by 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 AI components.
- Continuously evaluate newly released frontier models for improvements in quality, latency, reliability, or cost.
- Develop confidence estimation, reflection, and continuous learning systems to improve agents over time using real-world feedback.
- 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 for safe deployment.
- Collaborate with infrastructure engineers to deploy AI systems securely within enterprise cloud environments.
- Build human-in-the-loop workflows combining 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.
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.