Senior/Staff Machine Learning Engineer, General Agents, Enterprise GenAI · Scale AI

Sat, 18 Apr 2026 15:57:55 GMT
Engineering

Scale AI · San Francisco, CA; New York, NY · $264,800-$331,000 USD · hybrid

As a Senior/Staff Machine Learning Engineer on the General Agents team, you'll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems.

You will work across the full agent lifecycle,from model and system design to evaluation, deployment, and iteration,bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

Key responsibilities include:

Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.

Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.

Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.

Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.

Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.

Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.

Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

Ideal candidates will have:

5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.

Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.

Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.

Proven proficiency in Python, including writing production-quality, testable, and maintainable code.

Experience building systems that integrate models with external tools, APIs, databases, and services.

Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.

Strong communication skills and comfort working in customer-facing or cross-functional environments.

Nice-to-haves include hands-on experience building AI agents using modern generative AI stacks, experience with agent frameworks, orchestration layers, or workflow systems, familiarity with evaluation, monitoring, and observability for LLM-powered systems in production, and experience deploying ML systems in cloud environments and operating them at scale.