About the Role
Design, build, and deploy high-performance recommendation models that power personalized user experiences at scale. Work closely with data scientists and engineers to improve relevance, engagement, and system efficiency.
Responsibilities
- Develop scalable machine learning models for recommendation engines
- Optimize ranking algorithms based on user behavior and feedback
- Collaborate with data engineering teams to ensure clean, timely feature pipelines
- Implement real-time inference systems for low-latency recommendations
- Evaluate model performance using A/B testing and statistical analysis
- Improve model accuracy through feature engineering and experimentation
- Monitor system performance and troubleshoot production issues
- Work with product teams to define success metrics for recommendations
- Integrate user context and session data into personalization models
- Maintain and iterate on existing ML pipelines and infrastructure
- Research and prototype new recommendation techniques and architectures
- Ensure models are efficient, scalable, and maintainable
- Contribute to documentation and knowledge sharing within the team
- Support deployment and monitoring of ML models in production
- Collaborate on data labeling and ground truth collection strategies
- Apply best practices in model versioning and reproducibility
- Optimize for both cold-start and long-term personalization
- Work with large-scale datasets to train and validate models
- Ensure alignment between business goals and recommendation logic
- Participate in code reviews and technical design discussions
- Use telemetry data to identify areas for model improvement
- Balance exploration and exploitation in recommendation strategies
- Implement fairness and bias mitigation techniques in models
- Stay current with advancements in recommender systems research
- Contribute to roadmap planning for ML-driven product features
Nice to Have
- PhD or advanced degree in computer science or related field
- Published research in machine learning or recommender systems
- Experience with reinforcement learning for recommendations
- Background in large-scale online learning systems
- Familiarity with graph-based recommendation methods
- Knowledge of privacy-preserving ML techniques
- Experience with multi-objective optimization in ranking
- Worked on cross-domain or transfer learning in recommendations
- Contributions to open-source ML projects
- Experience with edge deployment of ML models
Compensation
Competitive salary and equity package
Work Arrangement
Remote-friendly with flexible hours
Team
Collaborative engineering team focused on machine learning and product innovation
About the Team
We are a tight-knit group of engineers and data scientists building intelligent systems that shape how users interact with digital content. Our focus is on creating fast, accurate, and scalable recommendation solutions that adapt to evolving user needs.
What We Value
We prioritize technical excellence, clear communication, and a deep curiosity for solving hard problems. We value individuals who take ownership, ship impactful work, and continuously improve both systems and themselves.
Available for qualified candidates
