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# Staff Machine Learning Engineer, Agentic

- **Company:** Unchained Labs
- **Location:** Menlo Park, CA, United States
- **Salary:** $255,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Machine Learning, Large Language Models, Reliability of Systems, Agentic-AI, Low Latency, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_04a837ade5c99a977c241a1fd885f29a

## About the Role

* Deep experience defining and measuring quality for agentic or machine learning systems using evaluation frameworks, datasets, and scorecards
 * Experience evaluating large language models or similar systems, including understanding tradeoffs in performance, cost, and latency
 * Demonstrated ability to analyze production issues and lead initiatives that improve system quality across multiple teams
 * Comfortable working with engineers, data scientists, and product partners to deliver measurable improvements in system performance

## Description

We are building an elite team applying frontier technologies to the world's biggest financial problems. Robinhood is where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Agentic AI team builds agentic AI systems that power intelligent, reliable customer experiences across Robinhood products. The team focuses on reducing the time to ship agents with fine-tuned models while enabling other teams to build, evaluate, and improve their own agents.

As a Staff Machine Learning Engineer (IC6), you will define and uphold the quality bar for agentic systems across the organization. You will design evaluation frameworks, guide model selection, and partner with product, data science, and engineering teams to ensure systems meet clear standards for correctness, safety, latency, and user satisfaction. Your work will shape how agentic systems are built, evaluated, and improved across Robinhood., * Define and implement evaluation frameworks that measure agent performance, including task success, correctness, tool usage reliability, latency, safety, and user satisfaction

 * Evaluate frontier and fine-tuned models across quality, latency, cost, and edge cases to determine appropriate use cases
 * Partner with product managers, data scientists, and engineers to translate evaluation results into clear launch criteria for agentic systems
 * Analyze production issues, identify root causes, and prioritize improvements to increase system reliability and performance
 * Build visibility into agent performance through metrics, monitoring, and reporting that inform roadmap decisions