Machine Learning Engineer

About the team + role

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. We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents.

You'll take on a highly influential role shaping vision and execution across key strategic initiatives. You'll partner with cross-functional leaders, contribute to high-impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting-edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them.

This role is based in our Bellevue, WA, with in-person attendance expected at least three 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

As a Machine Learning Engineer on the AI Research and Development team, the primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies.

The responsibilities will include:

• AI and ML Research: Evaluate cutting technologies, including but not limited to, transformer-based model architecture and large foundational models to identify solutions for Robinhood specific problems.
• 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.

What you bring

• Bachelor’s degree or foreign equivalent in Computer Science or related field and three years of experience in job offered or related occupation.
• Education and/or experience must include:
• Productionisation of ML models with focus on recommendations, ranking, or personalization;
• Model development with classical ML techniques for tabular data;
• Model development with modern ML techniques for sequential data;
• Hands-on experience with architectural frameworks of large, distributed, and high-scale ML applications;
• Produce robust business outcomes through comprehensive AB test and rigorous statistical analysis;
• Proficiency in Python, SQL, XGBoost, Pytorch or Tensorflow to carry out production ready projects; and
• Spark, Kafka, or Kubernetes.