About the Role
The role involves developing, deploying, and maintaining scalable machine learning models focused on recommendation engines, with an emphasis on performance, accuracy, and integration within existing platforms.
Responsibilities
- Design and implement machine learning models for personalized recommendations
- Optimize algorithms for speed and scalability across distributed systems
- Collaborate with data scientists and engineers to integrate models into production
- Analyze user behavior data to inform model improvements
- Monitor model performance and implement updates as needed
- Work with large datasets to train and validate machine learning systems
- Develop data pipelines to support real-time recommendation features
- Ensure models comply with privacy and data usage standards
- Conduct A/B testing to evaluate recommendation effectiveness
- Troubleshoot issues in model deployment and inference pipelines
- Document technical designs and model behavior for team reference
- Stay current with advancements in ML and recommendation techniques
- Participate in code reviews and system architecture discussions
- Support the migration of models from prototype to production
- Improve data quality and feature engineering processes
- Collaborate with product teams to align recommendations with business goals
- Evaluate trade-offs between model complexity and performance
- Use statistical methods to validate model outputs
- Contribute to system reliability and monitoring practices
- Mentor junior engineers on machine learning best practices
Nice to Have
- PhD in machine learning, computer science, or related area
- Experience with large-scale recommendation platforms
- Contributions to open-source ML projects
- Published research in machine learning or recommender systems
- Leadership experience in technical projects
- Familiarity with MLOps practices
- Experience with feature store technologies
- Knowledge of causal inference methods
- Background in user behavior modeling
Compensation
Competitive salary based on experience
Work Arrangement
Hybrid remote
Team
Collaborative engineering and data science team
What We Value
- Technical excellence paired with practical problem-solving
- Curiosity and a drive to learn from data
- Collaboration across disciplines to achieve shared goals
- Ownership of projects from concept to deployment
- Clear communication in both technical and non-technical settings
Impact
- Your work will directly influence how users discover content
- Models you build will scale to millions of interactions
- You'll help shape the future of personalized experiences
Available for qualified candidates
