Frontier Agents Engineer

About the role

This position involves bridging the gap between advanced AI research and real-world production systems. You will collaborate with enterprise clients to design, test, and implement intelligent agents that integrate large language models with complex business data and workflows.

Key facts

What you'll do

• Develop and deploy production-ready AI agents using LLMs, memory systems, and retrieval pipelines.
• Create evaluation frameworks including LLM-as-a-Judge, regression suites, and A/B testing to measure business impact.
• Architect systems that combine deterministic software, machine learning, and structured knowledge for enterprise use.
• Build multi-agent workflows that handle planning, tool execution, and human-in-the-loop oversight.
• Partner with customers to translate ambiguous business requirements into technical AI architectures.
• Implement observability, guardrails, and monitoring to ensure system reliability and safety.

Requirements

• 4+ years of experience in software engineering, machine learning, or applied AI.
• Proficiency in Python programming.
• Demonstrated experience building production-grade AI systems using LLMs.
• Familiarity with AI tooling such as OpenAI, Claude, MCP, agent frameworks, or vector databases.
• Strong grasp of modern language models and machine learning fundamentals.
• Ability to design quantitative metrics for AI evaluation.
• Communication skills suitable for direct interaction with enterprise customers.

Nice to have

• Experience building autonomous systems or production AI agents.
• Expertise in RAG, semantic search, knowledge graphs, or structured data representations.
• Background in model optimization, fine-tuning, distillation, or small language models.
• Experience with cloud infrastructure like AWS, Azure, or GCP.
• Proficiency with Docker, Kubernetes, and CI/CD pipelines.
• Experience leading technical architecture reviews or customer design workshops.

Skills & tools

• Python
• LLMs (OpenAI, Claude)
• Agent frameworks
• Vector databases
• RAG and retrieval systems
• Cloud platforms (AWS, Azure, GCP)
• Docker and Kubernetes
• Observability and evaluation frameworks

Practical notes

Compensation includes base salary, equity, and benefits such as health, dental, vision, retirement plans, and a learning stipend. Candidates are subject to a 90-day waiting period before they can be reconsidered for the same role. Please contact accommodations@scale.com if you require assistance during the application process due to a disability.