Senior Frontier Agents Engineer (Applied AI)

About Scale 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.

The Opportunity

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 Senior 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.

What You'll Build

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'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

We believe the fastest way to grow as an Frontier Agents engineer is to solve many different AI problems, not the same problem repeatedly. You'll work across diverse industries, datasets, model architectures, and agentic systems, rapidly developing intuition for what makes AI systems successful in production.

Required Qualifications

• 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 Qualifications

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.

Software Engineering

• 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.

Customer Engineering

• 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.

Dual Fluency

While this role initially emphasizes Applied AI and machine learning, every Frontier Agent Engineer develops expertise across both Applied AI and Forward Deployed Engineering.

Over time, you'll gain hands-on experience integrating AI systems into enterprise environments, deploying production infrastructure, and working directly with customer engineering teams. Our goal is to develop engineers who can move seamlessly between cutting-edge AI research and real-world production systems.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

$216,000—$270,000 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at View email address on aiapply.co. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision .

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