About the Role
The Lead Machine Learning Engineer will spearhead the creation and refinement of recommendation systems, ensuring they are accurate, efficient, and aligned with user engagement goals through close collaboration with engineering and product teams.
Responsibilities
- Lead the design and implementation of machine learning models for recommendation engines
- Collaborate with data engineers to build scalable data pipelines
- Optimize model performance and latency for real-time inference
- Mentor junior engineers and contribute to technical decision-making
- Work closely with product teams to align ML capabilities with user needs
- Evaluate new algorithms and techniques for improving recommendation accuracy
- Monitor system performance and troubleshoot production issues
- Ensure models comply with ethical and privacy standards
- Drive A/B testing frameworks to validate model improvements
- Document architecture decisions and model behavior for reproducibility
- Integrate feedback loops to enable continuous learning in production models
- Balance exploration and exploitation in recommendation strategies
- Support deployment of models across multiple platforms and devices
- Lead technical discussions on scalability, reliability, and maintainability
- Contribute to roadmap planning for long-term ML initiatives
- Analyze user interaction data to identify patterns and opportunities
- Implement personalization features based on user behavior and preferences
- Ensure efficient use of computational resources in training and serving
- Collaborate on feature engineering using behavioral and contextual signals
- Maintain up-to-date knowledge of advancements in recommender systems
- Promote best practices in ML engineering across the organization
- Assess third-party tools and libraries for potential integration
- Design evaluation metrics tailored to business objectives
- Support cross-functional initiatives involving data governance and compliance
- Facilitate knowledge transfer between teams through workshops and documentation
Nice to Have
- PhD in a relevant technical discipline
- Published research in machine learning or recommender systems
- Experience with reinforcement learning for recommendations
- Background in large-scale distributed systems
- Contributions to open-source ML projects
- Experience in high-growth startup environments
- Knowledge of natural language processing for content understanding
- Familiarity with edge computing for on-device recommendations
- Experience with multi-objective optimization in ranking models
- Understanding of fairness and bias mitigation in AI systems
Compensation
Competitive salary with performance bonuses and equity options
Work Arrangement
Hybrid work model with flexible scheduling
Team
Collaborative team of data scientists, engineers, and product specialists focused on AI-driven solutions
About the Role
This position leads the technical direction of recommendation systems that power personalized user experiences. The engineer will own the full lifecycle of ML models, from ideation to deployment, and work cross-functionally to ensure alignment with product goals.
What We Value
We prioritize technical excellence, clear communication, and a user-first mindset. Candidates should demonstrate a history of solving complex problems with scalable, maintainable solutions and a commitment to ethical AI practices.
Available for qualified candidates
