ML engineer on Robinhood's AI Research and Development team building ranking, recommendation, and bandit-driven decision systems

This Bellevue, WA-based (3 days/week in office) Machine Learning Engineer role sits on Robinhood's AI Research and Development team, focused on ranking/recommendation model development, reinforcement learning and multi-armed bandit strategies, and rigorous A/B testing. The JD asks for a Bachelor's + 3 years (or Master's + 1 year) with hands-on production experience in classical and sequential-data ML, plus Python/SQL/XGBoost/PyTorch or TensorFlow and distributed systems tools (Spark, Kafka, Kubernetes). Expect questions on production model development, experimentation rigor, and cross-functional delivery despite the JD's senior-sounding language about mentoring and shaping vision.

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What this interview tests

Ranking and recommendation systems (collaborative/content-based/hybrid filtering, LTR)
Reinforcement learning and multi-armed bandit strategies
A/B test design, execution, and statistical analysis
Classical ML on tabular data and modern ML on sequential data
Distributed, high-scale ML infrastructure (Spark, Kafka, Kubernetes)
Cross-functional delivery with data scientists, engineers, and marketing

Common question themes

Design a ranking or recommendation model for a fintech product surface

Explain how you'd apply a multi-armed bandit to a live decision-making problem

Walk through an A/B test you designed end-to-end, including how you validated significance

Compare classical tabular ML techniques vs modern sequential-data approaches you've used

How would you scale a training/inference pipeline using Spark, Kafka, or Kubernetes

Describe a reusable ML library or tool you built and how others adopted it

How candidates describe it

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