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# Senior Staff Machine Learning Engineer

- **Company:** Unchained Labs
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Salary:** $298,000.0 - $350,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning, Large Language Models, Deep Learning, Reliability of Systems, Low Latency, Transformer Architectures, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_27203180b6a7032babe053b1bdf01d93

## About the Role

* Experience building complex and impactful production ML models and systems, including understanding tradeoffs in performance, cost, and latency
 * Experience with traditional ML models, and also deep learning models; LLMs and Transformer models is a definite plus
 * Deep experience defining and measuring quality for machine learning systems using evaluation frameworks, datasets, and scorecards
 * 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

The AI team builds models and systems that power intelligent, reliable customer experiences across Robinhood products. The team focuses on accuracy, reliability and impact of ML models while enabling other teams to build, evaluate, and improve their own products.

As a Senior Staff Machine Learning Engineer, you will define and uphold the quality bar for ML 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 ML models are built, evaluated, and improved across Robinhood.

This role is based in our Bellevue, WA or Menlo Park, CA office, with in-person attendance expected at least 3 days per week., * Build and evaluate self-built, 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 AI systems
 * Analyze production issues, identify root causes, and prioritize improvements to increase system reliability and performance
 * Build visibility into model performance through metrics, monitoring, and reporting that inform roadmap decisions