Machine Learning Engineer

About the role Insights from data power most decisions at Robinhood. The Core ML team works with a simple mission of making it easy to use machine learning at Robinhood. The team is executing the mission by building the core infrastructure (eg. training and serving platform, feature platform) and a number of model-as-a-service solutions (eg. embedding service, multi-arm bandit service). The team works closely with data scientists who are applying ML in various spaces such as risk and fraud, growth, customer understanding etc. to ensure that they are able to ship their solutions to create business value.

As a machine learning engineer focused on applied ML, you will work closely with other teams to identify critical problems that can be solved using ML. You will co-develop a model, sometimes an ML system, with them and then, whenever possible, build and deploy the solution as a reusable and generalizable ML service. You will continue to build such services and onboard new applications to existing services over time.

What you’ll do day to day: Dive into data to understand business problems that may benefit from ML Train novel machine learning models and take them to production Invest in feature engineering and model hyper-parameter tuning to improve predictive power and performance of the models After deployment, closely monitor the model and take recourse to model interpretability to understand how its impacts on users Work cross-functionally with data scientists, product managers, operations, and other engineering teams to build generalizable and reusable models Collaborate with our data infrastructure teams to build highly scalable services and systems Present ML success stories to internal and external audiences Survey the latest and greatest in various areas of ML and bring the benefits of those to the firm whenever possible

About you: MS/PhD and 2+ years of industry experience as Machine Learning Engineer preferred Bachelors and 5+ years of industry experience as Machine Learning Engineer preferred Solid understanding of machine learning and deep learning algorithms Experience of working with large, noisy and highly imbalanced datasets Experience of building and shipping ML models that are aligned with product roadmap Excellent programming skills, including proficiency in Python and/or Go Passion for working and learning in a fast-growing company Excellent communication skills to tell a story through data. Experience of building relationships and influencing stakeholders across multiple discipline

Bonus points: Industry experience of delivering business impact with ML models or systems Research and publication experience in any field of machine learning Proof of excellence in Kaggle or similar forums

Technologies we use: Python Go Scikit-learn Tensorflow or Pytorch Kubernetes Kafka