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engineering

Posted 1 weeks ago

Software Developer, Ops Platform and Fraud Investigations

at Robinhood

Toronto, CanadaOn-site

Responsibilities

  • You will build tools that process large datasets and generate insights to support fraud investigation and decision-making

Requirements

  • The Ops Platform organization develops internal platforms that replace repetitive manual processes with AI-driven systems.
  • You will collaborate with data scientists and machine learning engineers to translate manual processes into automated systems.
  • You will work with data scientists and machine learning engineers to implement systems that automate manual review processes
  • You will design and develop AI-based applications that improve the speed and accuracy of operational tasks
  • experience designing and scaling distributed systems with a focus on reliability and performance You have
  • experience building and operating applications that use large language models or similar AI systems in production environments
  • You understand how to structure systems that manage model behavior and ensure consistent, reliable outputs You have
  • experience working with large-scale data pipelines and extracting meaningful insights from complex datasets
  • Access to the best AI tools on the market and continuous AI skill-building for every employee, technical or not
  • experience with catered meals, events, and comfortable workspaces.
  • AI Usage Disclosure: Robinhood uses artificial intelligence (AI) tools to support parts of our recruiting process.

Benefits

  • An estimated $124 trillion of assets will be inherited by younger generations in the next two decades.
  • Performance driven compensation with multipliers for outsized impact, bonus programs, and equity ownership Top tier
  • benefits to fuel your work, including supplemental health insurance, ancillary insurance, and mental health support programs
  • Lifestyle wallet - a highly flexible employer-paid
  • benefits such as wellness, childcare, learning, and more.
  • Time off to recharge including company holidays, paid time off, sick time, paid volunteer time off, parental leave, and more! Exceptional office
  • Monthly commuter stipend to help offset in-office commuting costs
  • Vacancy Notice: This job posting represents an existing vacancy that we are actively seeking to fill.
  • 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 be performed. Base Pay Range:
  • Toronto, ON $136,000 — $160,000 CAD

Additional details

  • If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.
  • We are building an elite team of bold thinkers and sharp problem-solvers who are wired to make an impact.
  • These tools support key areas such as Fraud Operations, Account Operations, Financial Crimes Operations, and Retirement Services.
  • The team works closely with product, data science, and operations partners to deliver reliable systems that improve decision-making and efficiency!
  • You will work with large datasets and signals to create tooling that supports fraud investigation and other operational workflows.
  • Your work will focus on improving system reliability, reducing operational effort, and increasing the speed at which new products and features can be supported across Robinhood’s offerings.
  • This role is based in our Toronto, ON 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. What you’ll do
  • You will define technical direction and make architectural decisions for systems that support operational workflows across multiple product lines

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