Research Scientist, Recommendation Systems
Hook
full-time
Posted on:
Location Type: Remote
Location: United States
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About the role
- Design, implement, and evaluate machine learning models for recommendation, ranking, and personalization
- Conduct applied research to improve model quality, robustness, and efficiency
- Develop and execute experimentation strategies using offline evaluation and online testing
- Work with complex datasets to understand user behavior and system performance
- Collaborate with engineering and product teams to transition research into production systems
- Share research results in clear, accessible ways with both technical and non-technical audiences
- Stay informed about advances in recommendation systems and related ML methodologies
Requirements
- Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience
- Experience developing or applying recommendation, ranking, or personalization models
- Strong foundation in machine learning, statistics, and data analysis
- Proficiency in Python and modern ML frameworks (e.g., PyTorch)
- Ability to conduct independent research and collaborate effectively in team environments
- Prior publications or submissions to RecSys or closely related venues
- Experience working with large-scale data and computational systems
- Experience with representation learning, sequence modeling, or large-scale optimization
- Experience transitioning research prototypes into production systems
Benefits
- Competitive base salary
- Meaningful equity ownership
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningrecommendation modelsranking modelspersonalization modelsdata analysisPythonPyTorchrepresentation learningsequence modelinglarge-scale optimization
Soft Skills
independent researchcollaborationcommunication
Certifications
PhDMS