Staff Frontier Agents Engineer (Applied AI)

Location: Not specified

Salary: $265,000–$331,000 USD per year, with potential equity and benefits.

Company

Scale AI provides data infrastructure and full-stack technologies for building, deploying, and overseeing reliable AI applications for enterprises and government organizations.

What you will do

• Design and deploy production AI agents using large language models, reasoning, retrieval, memory, tool use, and structured knowledge.
• Build customer intelligence layers, retrieval pipelines, knowledge representations, and multi-agent systems for complex enterprise workflows.
• Own experimentation and evaluation from hypothesis generation through production rollout, including benchmarks, A/B tests, regression suites, and human evaluation.
• Engineer reliable production systems with observability, latency and cost controls, safety guardrails, fallback strategies, and human-in-the-loop workflows.
• Work directly with enterprise customers to translate ambiguous business problems into AI architectures and scalable production solutions.
• Evaluate new models, prompting methods, reasoning techniques, and agent architectures based on quality, reliability, latency, cost, and business outcomes.

Requirements

• 8+ years of software engineering, machine learning, or applied AI experience.
• Strong Python programming skills and experience building production AI systems with LLMs.
• Experience with modern AI tooling such as 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 with quantitative metrics.
• Excellent communication skills and the ability to work directly with enterprise customers.

Nice to have

• Experience building AI agents or autonomous systems and designing evaluation frameworks for LLMs and agentic systems.
• Experience with RAG, semantic search, knowledge graphs, structured knowledge, fine-tuning, distillation, reinforcement learning, or model optimization.
• Experience with distributed systems, AWS, Azure, GCP, Docker, Kubernetes, CI/CD, and production observability.
• Experience leading technical workshops, architecture reviews, or customer design sessions.
• Familiarity with multimodal AI and frontier foundation models.

Culture & Benefits

• Opportunity to work across diverse AI challenges, industries, datasets, model architectures, and agentic systems.
• Health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous paid time off.
• Potential equity compensation for eligible roles and a possible commuter stipend.
• Inclusive equal-opportunity workplace with reasonable accommodations available during the application and recruiting process.

Hiring process

• Compensation is determined during the interview process based on location, skills, experience, qualifications, interview performance, and education or training.
• Candidates must wait 90 days before being reconsidered for the same role after an application.