Senior Software Engineer, AI Infrastructure

Job Description

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets 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. About the team + role 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. The AI Infrastructure team’s mission is to provide a robust, agile, and centralized AI platform—empowering teams across Robinhood to rapidly build, deploy, and iterate on high-quality Machine Learning (ML) and Generative AI (GenAI) applications at scale. As a Senior Software Engineer, you’ll help evolve our ML platform and services to support scalable, reliable, and secure model development and deployment. You’ll work closely with Data Scientists and Applied ML Engineers to build foundational systems that accelerate experimentation, improve model observability, and drive business impact through AI. This role is based in our Menlo Park, CA or Bellevue, WA office(s), with in-person attendance expected at least 3 days per week. 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. What you’ll do Design, build, and maintain scalable systems for deploying, monitoring, and managing Machine Learning models in production. Partner with ML practitioners to streamline workflows, integrate internal ML libraries, and optimize model performance. Contribute to the development and scaling of our feature store, enabling efficient feature retrieval across real-time and batch use cases. Implement robust observability for model performance, data pipelines, and feature freshness. Manage and optimize cloud compute resources (CPU/GPU) to support cost-effective training and inference across AWS. What you bring 4+ years of software engineering experience, ideally within ML infrastructure, data engineering, or model operations. Hands-on experience with model serving, distributed systems, or production ML workflows. Fa

Required Skills