Senior Machine Learning Engineer, Agentic

Robinhood
Bellevue, WA; Menlo Park, CAPosted 24 February 2026

Job Description

<div class="content-intro"><h2>Join us in building the future of finance.</h2> <p>Our mission is to democratize finance for all. <a href="https://www.cerulli.com/press-releases/cerulli-anticipates-124-trillion-in-wealth-will-transfer-through-2048" target="_blank">An estimated $124 trillion of assets</a> will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.</p></div><div id="C0930A04RMM-1754413700.256529-thread-list-Thread_1754413730.933709" class="c-virtual_list__item" data-qa="virtual-list-item" data-item-key="1754413730.933709"> <div class="c-message_kit__background c-message_kit__message c-message_kit__thread_message" data-qa="message_container" data-qa-unprocessed="false" data-qa-placeholder="false"> <div class="c-message_kit__hover" data-qa-hover="true"> <div class="c-message_kit__actions c-message_kit__actions--default"> <div class="c-message_kit__gutter"> <div class="c-message_kit__gutter__right" data-qa="message_content"> <div class="c-message_kit__blocks c-message_kit__blocks--rich_text"> <div class="c-message__message_blocks c-message__message_blocks--rich_text" data-qa="message-text"> <div class="p-block_kit_renderer" data-qa="block-kit-renderer"> <div class="p-block_kit_renderer__block_wrapper p-block_kit_renderer__block_wrapper--first"> <div class="p-rich_text_block"> <div class="p-rich_text_section"> <h2><strong>About the team + role<br></strong></h2> <p>We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s 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.</p> <p>The Agentic team at Robinhood builds and ships production AI agents that power the next generation of AI financial products. Our mission is to rapidly build, evaluate, and deploy high-performance AI agents on production-grade infrastructure, strong evaluation and observability baked in, and continuous optimization support. </p> <p><strong>This role is based in our Menlo Park, CA and Bellevue, WA offices, with in-person attendance expected at least 3 days per week.</strong></p> <p>At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.</p> <h2><strong>What you’ll do</strong></h2> <ul> <li>Translate product goals into measurable metrics and SLOs, and build a rigorous evaluation harness to continuously score agents performance</li> <li>Develop feedback and optimization pipelines that uses both automated metrics and human-in-the-loop evaluation signals to improve agent behavior over time</li> <li>Implement and scale optimization techniques such as Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and reward modeling to improve agent performance.</li> <li>Launch and support fine-tuned models in production environments with robust evaluation, rollback strategies, and performance monitoring.</li> <li>Collaborate closely with applied AI/ML teams to translate state-of-the-art research in agentic reasoning, planning, and tool use into reliable, production-ready systems</li> </ul> <h2><strong>What you bring</strong></h2> <ul> <li>Strong technical expertise in software development, with understanding of agentic workflows—including reasoning loops, tool invocation, memory, and orchestration of autonomous AI agents.</li> <li>Hands-on experience using Large Language Models, including prompt engineering, fine-tuning, model distillation, and deploying optimized models (e.g. via DPO, PPO) i ... (truncated, view full listing at source)