Senior Frontier AI Engineer for Production Agents

Front Door Defense; New York (NY); On-site; USD 252,000 - 315,000; Full time

Benefits offered by this job

Comprehensive health

Dental and vision coverage

Retirement benefits

Learning and development stipend

Commuter stipend

Job summary

Scale AI is hiring a Staff Frontier Agents Engineer (Applied AI) to design, deploy, and scale production frontier AI agents for enterprise clients. You will bridge research and production, working with customers to architect systems that combine frontier models, structured data, and traditional ML in secure cloud environments.

You will own the experimentation lifecycle, evaluate models, and build robust, observable pipelines.

Qualifications

• 8+ years of software engineering, ML, or applied AI experience.
• Strong Python programming skills.
• Experience building production AI systems using LLMs.
• Experience with modern AI tooling (OpenAI, Claude, MCP, or similar) and retrieval systems.
• Strong understanding of ML fundamentals and language models.
• Experience designing/evaluating AI systems with quantitative metrics.
• Excellent communication with enterprise customers.

Responsibilities

• Design and deploy production frontier AI agents for enterprise clients.
• Architect intelligent systems that combine LLMs, knowledge, and deterministic software.
• Develop multi-agent systems and retrieval/memory components.
• Evaluate frontier models and agent architectures in production contexts.
• Collaborate with customers to translate problems into scalable AI architectures.

Skills

Python

Production AI systems

LLM tooling

Enterprise software

Tools

Docker

Kubernetes

CI/CD

Vector databases

Job description

Scale AI is hiring a Staff Frontier Agents Engineer (Applied AI) to design, deploy, and scale production frontier AI agents for enterprise clients. You will bridge research and production, working with customers to architect systems that combine frontier models, structured data, and traditional ML in secure cloud environments.

You will own the experimentation lifecycle, evaluate models, and build robust, observable pipelines.