> Markdown version of [/jobs/ext/463607-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/463607-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Robinhood
- **Location:** Bellevue, WA, United States
- **Experience:** Experienced
- **Salary:** $161,138.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Data Analysis, Experimental Data, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, SQL Databases, Reinforcement Learning, Pytorch, Apache Spark, Kubernetes, Information Technology, Xgboost, Apache Kafka, GPT
- **Published:** June 4, 2026
- **Apply:** https://seeker-sp.worksourcewa.com/jobview/GetJob.aspx?JobID=293460412

## About the Role

Minimum Requirements for Position: Bachelor's degree or foreign equivalent in Computer Science or related field and three years of experience in job offered or related occupation. Alternatively, a Master's in Computer Science or related field and one year (1) of experience in job offered or related occupation.

Education 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.

## Description

Robinhood Markets, Inc. has an opening in Bellevue, WA for Machine Learning Engineer (10035097):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.


## Related Videos

- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation)
- [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)