Staff Software Engineer, Machine Learning Risk & Fraud
Menlo Park, CA
Robinhood was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood is 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.
Just as we focus on our customers, we also strive to create an inclusive environment where our employees can thrive and do impactful work. We are proud of the competitive products and company culture we continue to build and have been recognized as:
- Glassdoor Best Places to Work 2020
- TIME100 Most Influential Companies 2021
- Fortune Best Workplaces in Financial Services & Insurance™ 2021 and Fortune Best Workplaces for Millennials™ 2021
We’re growing and looking for...
We continue to hire Robinhoodies at a rapid pace to drive this journey, and with that growth comes necessary change. We’re seeking culture builders and curious thinkers looking to co-author the next chapters of our story. We’re in build mode, majorly expanding our team while also growing up as a company. Joining now means helping shape our structures and systems, then taking part as we launch into our ambitious future.
Check out life at Robinhood on The Muse!
About the team:
Insights from data power most decisions at Robinhood. The Risk and Fraud team works on protecting the firm from many risks like abuse and fraud loss. As a machine learning engineer, you will own, build, deploy and monitor , data-driven, fraud defense solutions that add predictability across Robinhood’s businesses, products and operations. The ideal candidate is passionate about creating innovative solutions and has an interest in solving business problems.
What you’ll do day-to-day:
- Dive into data to understand the business problem.
- Train novel machine learning models and take them to production.
- Story based feature engineering and model hyper-parameter tuning to improve predictive power and performance of the models.
- Model interpretability to understand how ML impacts our users.
- Work cross-functionally with product managers, operations and other engineering managers in order to build end-to-end solution.
- Collaborate with our data infrastructure teams to build a highly scalable machine learning platform.
- 7+ years of experience as machine learning engineer or applied machine learning scientist
- Solid understanding of machine learning and deep learning algorithms
- Graduate degree in a quantitative field such as mathematics, statistics, engineering or natural sciences and some experience in software engineering in a production environment.
- Excellent programming skills, including proficiency in Python, Scala, Java or C++ programming languages.
- Passion for working and learning in a fast-growing company
- Excellent communication skills to tell a story through data.
- Experience building relationships and influencing stakeholders across multiple disciplines
- Experience working with product teams.
- Experience working with datasets that reflect real life problems such as noisy, highly imbalanced datasets
- Experience with impactful ML experience in the field of Risk & Fraud
- Experience with distributed data platforms, such as Spark or Kafka
- Experience with ML deployment frameworks such as MLflow or Kubeflow
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's benefits include generous time off, 401(k) participation with employer match, comprehensive health coverage, a health savings account (HSA), wellness benefits, backup childcare and education stipends (all benefits are subject to applicable taxes and based on eligibility).
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