Senior/Staff Machine Learning Engineer, Scale AI

• 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

Desirable

• The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications
• The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value
• Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers
• As a Senior/Staff Machine Learning Engineer (MLE) 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
• 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