Senior Frontier Agents Engineer (Applied AI)

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.

About the role

The Applied AI field is evolving rapidly, with new foundation models, reasoning techniques, agent architectures, and research emerging weekly. As a Senior Frontier Agent Engineer (Applied AI), you will bridge the gap between cutting-edge AI research and production deployment. You will collaborate directly with enterprise customers to design, evaluate, and deploy intelligent systems that integrate frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. This role involves tackling diverse AI challenges across multiple industries, from building multi-agent research systems to designing customer intelligence platforms, healthcare copilots, or autonomous workflows for Fortune 100 companies.

Responsibilities

• Design and deploy production AI agents leveraging 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 for agents to reason over large, heterogeneous enterprise data.
• Develop multi-agent systems coordinating 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 assess the impact of models, prompts, retrieval strategies, reasoning techniques, memory systems, and agent architectures.
• Continuously evaluate newly released frontier models for improvements in quality, latency, reliability, or cost.
• Develop confidence estimation, reflection, and continuous learning systems to enhance agents using real-world feedback.
• Measure success through business outcomes, not just benchmark scores.
• Build production-quality AI systems with a focus on reliability, observability, latency, safety, and cost.
• Design agent guardrails, fallback strategies, tracing, monitoring, and evaluation pipelines for safe deployment in high-stakes environments.
• Collaborate with infrastructure engineers to securely deploy AI systems in enterprise cloud environments.
• Build human-in-the-loop workflows that 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 enterprise deployments.

Qualifications

Required

• 5+ 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.

Preferred

• 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.
• 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.

Skills

This role emphasizes both Applied AI and Forward Deployed Engineering. Over time, you will develop expertise in integrating AI systems into enterprise environments, deploying production infrastructure, and collaborating with customer engineering teams. The goal is to cultivate engineers who can seamlessly transition between cutting-edge AI research and real-world production systems.

Benefits

• Comprehensive health, dental, and vision coverage.
• Retirement benefits.
• Learning and development stipend.
• Generous PTO.
• Commuter stipend (role-dependent).

Pay

The base salary range for this full-time position in San Francisco, New York, or Seattle is $216,000—$270,000 USD. Compensation packages include base salary, equity, and benefits, with specific details determined during the interview process based on job-related skills, experience, qualifications, interview performance, and relevant education or training.