Engineering Manager, Financial Crimes

Robinhood Robinhood · Fintech · Menlo Park, CA · ENG Platforms

Robinhood is seeking an Engineering Manager for their Financial Crimes team. This role involves leading a team to build and maintain systems for anti-money laundering (AML) and sanctions compliance. The manager will oversee the end-to-end delivery of detection platforms, drive detection strategy, and improve investigation workflows using AI and LLMs. The role requires strong technical depth in backend systems (Golang, Python, AWS, Kubernetes, SQL) and experience leading engineering teams. Familiarity with financial crimes or compliance systems is preferred, as is working knowledge of ML/generative AI.

What you'd actually do

  1. Lead, mentor, and grow a team of engineers (IC3–IC5), setting clear goals, providing regular feedback, and supporting career development
  2. Build a top-tier tooling experience, leveraging AI to streamline workflows, improve analyst efficiency, and accelerate investigation operations.
  3. Guide onboarding of new financial crimes use cases into our case management systems, ensuring reliability, scalability, and regulatory alignment.
  4. Contribute to architectural decisions and remain technically engaged in systems built with Golang, Python, AWS, Kubernetes, and SQL-based data stores.
  5. Partner with Financial Crimes Operations and peer engineering teams to translate regulatory and investigative requirements into technical solutions.

Skills

Required

  • Experience leading and developing engineers in a high-growth technology environment, with a track record of delivering multi-quarter technical initiatives.
  • Strong technical depth in backend systems, with hands-on experience in Golang (primary), Python, AWS, Kubernetes, and SQL.
  • Ability to collaborate effectively with operations, product, and engineering partners to deliver systems that meet regulatory and business requirements.

Nice to have

  • Experience building or supporting financial crimes, fraud, compliance, or fintech systems; familiarity with AML or sanctions screening workflows is preferred.
  • Experience designing and delivering data-intensive systems, ideally including detection platforms, data pipelines, or internal tooling.
  • Working knowledge of machine learning or generative AI concepts, with the ability to evaluate and apply them to platform use cases.

What the JD emphasized

  • applying AI and large language models to improve investigation workflows and system performance

Other signals

  • applying frontier technologies to the world’s biggest financial problems
  • building next-generation detection systems
  • applying AI and large language models to improve investigation workflows and system performance