Senior Machine Learning Engineer

at  Robinhood

JOIN A LEADING FINTECH COMPANY THAT’S DEMOCRATIZING FINANCE FOR ALL.

Robinhood Markets was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood and its subsidiaries and affiliates are lowering barriers and providing greater access to financial information. Together, we are building products and services that help create a financial system everyone can participate in.

WITH GROWTH AS THE TOP PRIORITY…

The business is seeking curious, growth-minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you’re invigorated by our mission, values, and drive to change the world — we’d love to have you apply.

WHAT WE OFFER

• Market competitive and pay equity-focused compensation structure
• 100% paid health insurance for employees with 90% coverage for dependents
• Annual lifestyle wallet for personal wellness, learning and development, and more!
• Lifetime maximum benefit for family forming and fertility benefits
• Dedicated mental health support for employees and eligible dependents
• Generous time away including company holidays, paid time off, sick time, parental leave, and more!
• Lively office environment with catered meals, fully stocked kitchens, and geo-specific commuter benefits
  Click here to learn more about available Benefits, which vary by region and Robinhood entity.
  We’re looking for more growth-minded and collaborative people to be a part of our journey in democratizing finance for all. If you’re ready to give 100% in helping us achieve our mission—we’d love to have you apply even if you feel unsure about whether you meet every single requirement in this posting. At Robinhood, we’re looking for people invigorated by our mission, values, and drive to change the world, not just those who simply check off all the boxes.
  Robinhood embraces a diversity of backgrounds and experiences and provides equal opportunity for all applicants and employees. We are dedicated to building a company that represents a variety of backgrounds, perspectives, and skills. We believe that the more inclusive we are, the better our work (and work environment) will be for everyone. Additionally, Robinhood provides reasonable accommodations for candidates on request and respects applicants’ privacy rights. Please review the specific Robinhood Privacy Policy applicable to the country where you are applying

ABOUT THE TEAM + ROLE

The mission of the Applied Machine Learning team is to provide scalable data and model driven decision making solutions to the various business functions at Robinhood. We aim to create a personalized experience for our users, by helping them discover & engage with the right products & features within Robinhood that they might find most valuable. To accelerate progress, we are also building an accessible model development platform to democratize machine learning practices throughout the company. As we embark on this exciting journey, we are looking for a senior Machine Learning Engineer to join us to make this vision a reality.

WHAT YOU’LL DO

As a Machine Learning Engineer on our team, your primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies. Your responsibilities will include:

• Model Development and Implementation: Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content-Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation.
• A/B Testing and Experimentation: Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results.
• Data Analysis and Insight Generation: Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy.
• Cross-Functional Collaboration: Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders.
• Tooling and Documentation: Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings. .