Senior Machine Learning Engineer, Agentic

Robinhood

Hybrid

Quick summary

Work type: Hybrid
Location: Bellevue, WA · Menlo Park, CA
Salary: $209,000–$245,000 / yr
Posted: 1 day ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $212k

This role $227k

$162k most similar roles pay here $255k

This role pays more than 68% of similar roles. Most pay $177,875–$246,150 — the shaded band above. At the midpoint, this role pays about $227k versus about $212k for comparable roles.

Based on 240 similar postings.

Employer

About Robinhood

Robinhood is a financial technology company offering commission-free stock, ETF, options, and cryptocurrency trading through its mobile app, aimed at democratizing access to financial markets. Industry: Financial Technology & Investment App

Robinhood currently has 78 open roles on FindRole.

Listed pay typically runs $187,000–$220,000 across 77 roles with salary data.

Most-posted roles

View all roles at Robinhood

At a glance

TL;DR · Senior Machine Learning Engineer, Agentic

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Join the Agentic team at Robinhood as a Senior Machine Learning Engineer, where you will focus on developing and deploying advanced AI agents for financial products. Your daily tasks include evaluating cutting-edge technologies like transformer models, implementing scalable ranking and recommendation systems using techniques such as Collaborative Filtering and Reinforcement Learning, conducting A/B tests to assess model performance, and collaborating with cross-functional teams to integrate these solutions into the product. You will also build reusable libraries and maintain comprehensive documentation for your work. Ideal candidates have 5+ years of experience in productionizing ML models, particularly in ranking and recommendation systems, and proficiency in Python, SQL, XGBoost, PyTorch/TensorFlow, with additional knowledge in Spark, Kafka, and Kubernetes being beneficial.

Skills

Python PyTorch TensorFlow Kubernetes Spark Kafka XGBoost SQL Collaborative Filtering Content-Based Filtering Hybrid Models Learning to Rank Reinforcement Learning Multi-Armed Bandit CI/CD

What you'll do

Evaluate and implement cutting-edge AI technologies for financial products.
Develop scalable machine learning models focusing on advanced ranking and recommendation systems.
Design and conduct A/B tests to assess the performance of different ML models.
Analyze experimental data to extract actionable insights using statistical techniques.
Build reusable libraries and maintain comprehensive documentation for ML practices.

What we're looking for

5+ years of applied machine learning experience, with a focus on recommendations and ranking systems.
Proven expertise in developing and implementing scalable ML models for advanced ranking and recommendation systems.
Hands-on experience with reinforcement learning algorithms and multi-objective optimization techniques.
Proficiency in Python, SQL, XGBoost, PyTorch/TensorFlow, and familiarity with Spark, Kafka, Kubernetes.
Strong background in evaluating cutting-edge AI technologies and conducting rigorous A/B testing for model performance.