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 the software that manages, governs, and reduces Robinhood's AWS cloud spend, operating at the intersection of cloud infrastructure, data engineering, and FinOps. The team owns the full lifecycle of cloud cost: the data platforms that make spend transparent and attributable, the anomaly detection and forecasting systems that make it predictable, the automation that continuously rightsizes infrastructure at fleet scale, and the capacity planning and commitment strategy that keep a bursty, latency-sensitive trading platform both reliable and cost-effective. Our work carries CEO-level visibility and has already driven millions of dollars in annualized savings. We partner closely with Data Science, Infrastructure, Finance, and product engineering teams across Robinhood to turn cost insight into real financial outcomes.

As a Staff Software Engineer on the team, you will serve as the technical lead for the Capacity arm: setting engineering direction, owning the most complex systems, and raising the bar for how the team builds and operates. You will design the platforms that attribute cloud costs to their real consumers, catch spend regressions before they compound, and forecast future demand and unit economics. Just as importantly, you will build the systems that act on those insights: automation that safely optimizes production infrastructure, and tooling that keeps our commitment portfolio matched to actual usage. You will work directly with senior leadership and partner organizations to translate complex cost data into clear accountability. This is a role for an engineer who leads from the front technically while shaping 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 capacity planning and demand forecasting for a latency-sensitive, bursty production environment, ensuring the platform scales to absorb peak demand without sustained over-provisioning
• →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
• →Shape how the company measures and governs fast-growing AI/ML infrastructure spend, and apply AI-driven automation to cost operations such as anomaly triage, reporting, and remediation tracking

• Strong data engineering skills, including distributed data processing (e.g., Spark), workflow orchestration (e.g., Airflow), and analytical data stores serving interactive workloads
• Strong proficiency with Kubernetes and a deep practical understanding of resource efficiency, capacity planning, and the relationship between infrastructure configuration and cloud spend
• Familiarity with cloud billing data and commercial constructs (usage and billing reports, Reserved Instance and Savings Plan amortization, enterprise agreements) is a strong plus
• Experience building automation that safely modifies production infrastructure, with guardrails, progressive rollout, and rollback built in, not just systems that observe and report
• 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
• Ability to communicate fluently with both engineering and finance audiences, translating infrastructure decisions into financial outcomes and presenting to senior leadership