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Scale AI
Senior/Staff Machine Learning Research Engineer, General Agents, Enterprise GenAI
Scale AI
San Francisco•On-site•5-8 Yrs•Jun 28
Job description
Overview
Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications.
Responsibilities
- 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.
- 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.
Qualifications
- 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.
- Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
- Interest in shaping the future of general-purpose enterprise agents and their real-world impact.
Nice to have
- Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).