Software Engineer, Agentic AI

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. We're looking for an exceptional Software Engineer, Agentic AI to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents. You'll take on a highly influential role shaping vision and execution across key strategic initiatives. You'll partner with cross-functional leaders, contribute to high-impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting-edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them. This role is based in our Bellevue, WA, with in-person attendance expected at least three 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 Architect and implement distributed and cloud-based systems that support the full machine learning lifecycle - from data ingestion and preprocessing to model training, deployment, and monitoring. Develop and maintain robust backend services and APls (RESTful and gRPC) in programming languages such as Python or Go. Optimize compute, storage, and data retrieval systems using relational, NoSQL, and vector databases to enable low-latency model inference and intelligent data access. Collaborate with cross-functional teams to deliver Al-driven capabilities in domains such as market data, trading, accounting, authentication, and security. Leverage cloud infrastructure (e.g., AWS or GCP) and container orchestration technologies (e.g., Docker, Kubernetes) to manage large-scale compute environments for Al workloads. Design and maintain automated Cl/CD workflows to ensure efficient and reliable code delivery. Implement model serving and observability systems to monitor performance, ensure fault tolerance, and improve system reliability. Apply algorithms and data structures to enhance compute and data efficiency across the ML infrastructure stack. Prototype and evaluate new Al tools, frameworks, and system designs to validate concepts and optimize performance prior to large scale rollout. Drive continuous improvement by identifying opportunities for scalability, performance, and cost optimization. Contribute to open-source projects (where possible) and uphold software engineering best practices including code reviews, automated testing, and version control in an agile environment. Telecommuting permitted. What you bring - Bachelor’s degree or foreign academic equivalent in Computer Science or a related field. - Education and/or experience must include: - Kubernetes - Spark - Machine Learning - Python - Databases - AWS. - Background checks required What we offer - Challenging, high-impact work to grow your career - Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching - Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents - Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more - Employer-paid life & disability insurance, fertility benefits, and mental health benefits - Time off to recharge including company holidays, paid time off, sick time, parental leave, and more! - Exceptional office experience with catered meals, events, and comfortable workspaces. In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits. Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will

Bachelor’s degree or foreign academic equivalent in Computer Science or a related field. Kubernetes, Spark, Machine Learning, Python, Databases, AWS.

Architect and implement distributed and cloud-based systems that support the full machine learning lifecycle from data ingestion and preprocessing to model training, deployment, and monitoring. Develop and maintain robust backend services and APIs (RESTful and gRPC) in Python or Go. Optimize compute, storage, and data retrieval systems using relational, NoSQL, and vector databases to enable low-latency model inference. Collaborate with cross-functional teams to deliver AI-driven capabilities in domains such as market data, trading, accounting, authentication, and security. Leverage cloud infrastructure (AWS or GCP) and container orchestration technologies (Docker, Kubernetes) to manage large-scale compute environments for AI workloads. Design and maintain automated CI/CD workflows. Implement model serving and observability systems to monitor performance and reliability. Apply algorithms and data structures to enhance compute and data efficiency across the ML stack. Prototype and evaluate new AI tools and system designs. Drive continuous improvement for scalability, performance, and cost optimization. Contribute to open-source projects where possible and uphold software engineering best practices in an agile environment. Telecommuting permitted.