Staff Software Engineer, Capacity and Efficiency Engineering

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.

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 Capacity & Efficiency Engineering team builds software to manage, govern, and reduce Robinhood's AWS cloud spend — operating at the intersection of cloud infrastructure, data engineering, and FinOps. The Capacity arm of the team owns the systems that make cloud costs transparent, predictable, and accountable: from multi-layer cost attribution and chargeback systems to automated anomaly detection, forecasting models, and AWS pricing strategy. This is a high-visibility team whose work directly impacts company-level cost efficiency, carries CEO-level visibility, and has already driven over $12M in annualized savings. We partner closely with Data Science, Infrastructure, TPMs, and product engineering teams across Robinhood to drive real financial outcomes at scale!

As a Staff Software Engineer on the Capacity & Efficiency Engineering team, you will serve as the technical lead for the Capacity arm — setting the engineering direction, owning the most complex systems, and raising the bar for how the team builds and operates. You will design and build the foundational platforms that attribute AWS costs to real consumers, detect spend anomalies before they become regressions, and surface the forecasting and unit economics insights that drive proactive cost governance across Robinhood. You'll partner deeply with Data Science on forecasting models and unit economics, collaborate with Infrastructure and product teams to validate attribution accuracy, and work directly with leadership and partner organizations to translate complex cost data into clear, actionable accountability. This is a role for an engineer who can lead from the front technically while influencing the direction of a team that operates at the highest levels of the company.

This role is based in our Bellevue, WA office, 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.

• →Lead the technical direction for the Capacity arm of the team — owning the architecture, roadmap, and engineering standards for systems that govern, attribute, and forecast Robinhood's AWS cloud spend
• →Design and build scalable cost attribution and chargeback systems that accurately identify true resource consumers across platform and product teams, enabling fair and transparent cost accountability
• →Architect and own automated anomaly detection and governance tooling that detects cost regressions, routes findings to the correct owning teams, and tracks remediation outcomes end-to-end
• →Partner closely with Data Science to develop and maintain forecasting models, year-over-year cost projections, and unit economics frameworks (e.g., cloud cost per engaged user) that surface future spend risk proactively
• →Drive Robinhood's AWS contracts and pricing strategy by partnering with Amazon on reserved capacity, discount models, and on-demand optimization to ensure cost-effective infrastructure investment

• Experience designing and building production-grade data or infrastructure systems at scale, ideally in a cloud-native environment on AWS, where cost, reliability, and operational efficiency are first-class concerns
• Strong proficiency with Kubernetes and a deep practical understanding of resource efficiency, capacity planning, and the relationship between infrastructure configuration and cloud spend
• Experience building systems in the FinOps, cost attribution, anomaly detection, or cloud governance space — or strong adjacent experience in data engineering, platform engineering, or infrastructure observability
• Ability to reason deeply about cloud cost models, including tradeoffs between reserved vs. on-demand capacity, instance efficiency, and the financial impact of infrastructure decisions at scale
• Demonstrated technical leadership experience — driving complex, cross-functional projects end-to-end, setting architectural standards, and influencing engineering direction across a team or organization