Role overview
We are building an elite team, applying frontier technologies to the worldâs biggest financial problems. Weâre looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isnât a place for complacency, itâs where ambitious people do the best work of their careers. Weâre a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.
The mission of the AI Research and Development 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.
This role is based in our Menlo Park, CA or Bellevue, WA office(s), 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 work on
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. .