Senior Software Engineer

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 Senior Software Engineer 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 Menlo Park, CA, 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 Design, develop, and maintain complex machine learning models and AI-powered services that support core product features and decision-making systems. Lead the design and implementation of end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment. Partner closely with product, data, and engineering teams to translate complex business problems into scalable machine learning solutions. Own ML components end to end, including experimentation, architecture, implementation, deployment, production monitoring, and iteration. Drive improvements in model performance, reliability, and scalability through rigorous experimentation, validation, and optimization. Design and oversee the integration of ML models into production systems, ensuring low-latency inference and reliable service operation. Define monitoring strategies for model performance and data quality, and lead debugging, incident response, and remediation efforts. Uphold high standards of software engineering in ML codebases through code reviews, testing strategies, and documentation. Ensure ML systems meet security, privacy, and compliance requirements for sensitive data handling. Author and maintain technical documentation for model designs, evaluations, and operational considerations. Telecommuting permitted. What you bring - Bachelor’s degree in Computer Science, Computer Engineering, Software Engineer or a related field (or foreign equivalent) and 5 years of progressively responsible experience in the job offered or related occupation. Alternatively, a Master’s degree in Computer Science, Computer Engineering, Software Engineer or a related field (or foreign equivalent) and 3 years of experience in the job offered or related occupation is acceptable. - Education and/or experience must include: - Python, Java, Scala, or GoLang; - Machine learning frameworks; - Data processing and feature engineering frameworks; - Supervised and unsupervised learning techniques; - ML pipeline and deployment tools; - Model monitoring and observability tools; and - Testing and artifact versioning tools. - 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

Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering or a related field (or foreign equivalent) and 5 years of progressively responsible experience in the job offered or related occupation. Alternatively, a Master’s degree in Computer Science, Computer Engineering, Software Engineering or a related field (or foreign equivalent) and 3 years of experience in the job offered or related occupation is acceptable. Education and/or experience must include: Python, Java, Scala, or GoLang; Machine learning frameworks; Data processing and feature engineering frameworks; Supervised and unsupervised learning techniques; ML pipeline and deployment tools; Model monitoring and observability tools; and Testing and artifact versioning tools. Background checks required.

Design, develop, and maintain complex machine learning models and AI-powered services that support core product features and decision-making systems. Lead the design and implementation of end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment. Partner closely with product, data, and engineering teams to translate complex business problems into scalable machine learning solutions. Own ML components end to end, including experimentation, architecture, implementation, deployment, production monitoring, and iteration. Drive improvements in model performance, reliability, and scalability through rigorous experimentation, validation, and optimization. Design and oversee the integration of ML models into production systems, ensuring low-latency inference and reliable service operation. Define monitoring strategies for model performance and data quality, and lead debugging, incident response, and remediation efforts. Uphold high standards of software engineering in ML codebases through code reviews, testing strategies, and documentation. Ensure ML systems meet security, privacy, and compliance requirements for sensitive data handling. Author and maintain technical documentation for model designs, evaluations, and operational considerations.