Senior/Staff Machine Learning Research Engineer (Enterprise GenAI)

TL;DR

Senior/Staff Machine Learning Research Engineer (Enterprise GenAI): Building and deploying production-ready general AI agents for recurring enterprise use cases with an accent on LLM reasoning, tool use, evaluation, and scalable system architecture. Focus on designing multi-step agent workflows, productionizing frontier techniques, and improving reliability through monitoring, failure analysis, and real-world iteration.

Location: San Francisco, New York, or Seattle, United States

Salary: $264,800–$331,000 USD base salary, plus potential equity and benefits.

Company

Scale AI develops data, generative AI, and enterprise AI technologies that help organizations build, deploy, and oversee reliable production AI applications.

What you will do

• Design and implement end-to-end agent systems combining LLM reasoning, tool use, memory, and control logic.
• Build scalable agent architectures that support multiple customers, data sources, tools, and operational constraints.
• Develop evaluation frameworks, datasets, environments, and metrics for agent performance, reliability, and business impact.
• Productionize planning, multi-step reasoning, tool-use, and multi-agent techniques into observable systems.
• Collaborate with product managers, customers, data annotators, and engineering teams to translate enterprise requirements into robust solutions.
• Own deployment, monitoring, failure analysis, continuous improvement, and technical direction for general agent development.

Requirements

• 5+ years of experience building and deploying machine learning or AI systems in production.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Strong understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Production-quality Python development experience, including testable and maintainable code.
• Experience integrating models with external tools, APIs, databases, and services.
• Ability to work in ambiguous problem spaces and communicate effectively with customers and cross-functional teams.

Nice to have

• Experience building AI agents with OpenAI APIs or commercial and open-source LLMs.
• Experience with agent frameworks, orchestration layers, workflow systems, tool calling, planners, or multi-agent setups.
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience with SFT, RLVR, or LoRA for adapting foundation models.

Culture & Benefits

• Inclusive equal-opportunity workplace focused on developing reliable AI systems.
• Comprehensive health, dental, and vision coverage.
• Retirement benefits, learning and development stipend, and generous paid time off.
• Potential equity compensation and commuter stipend for eligible roles.

Hiring process

• Compensation level and location-specific salary are determined during the interview process based on experience, qualifications, and interview performance.
• Candidates must wait 90 days before being reconsidered for the same role.