About the Role
Lead the development and deployment of scalable machine learning models for recommendation systems, driving innovation and operational excellence across the full ML lifecycle.
Responsibilities
- Design and implement machine learning models to power personalized recommendation experiences
- Collaborate with data scientists and engineers to translate research into production systems
- Optimize model performance, latency, and scalability in live environments
- Lead the end-to-end ML development lifecycle from concept to deployment
- Develop robust data pipelines to support training and inference workflows
- Apply software engineering best practices to ML codebases
- Monitor model behavior and implement retraining strategies
- Work closely with product teams to align technical solutions with business goals
- Evaluate new algorithms and techniques for improving recommendation quality
- Ensure models are explainable, fair, and aligned with ethical standards
- Drive adoption of ML Ops practices across teams
- Mentor engineers in machine learning design and implementation
- Troubleshoot production issues related to data, models, or infrastructure
- Contribute to architectural decisions for scalable ML systems
- Integrate models with frontend and backend services
- Use A/B testing to validate model impact
- Maintain documentation for models and systems
- Stay current with advancements in recommendation algorithms
- Ensure compliance with data privacy requirements
- Support model governance and auditability
- Work with large-scale datasets efficiently
- Apply distributed computing frameworks when needed
- Optimize for cost-effective model serving
- Improve system reliability and uptime
- Collaborate across disciplines to deliver end-to-end solutions
Nice to Have
- Master's or PhD in a technical field related to machine learning
- Experience leading machine learning projects or teams
- Publications or contributions in ML or recommender systems
- Deep knowledge of neural networks and deep learning
- Experience with large-scale online learning systems
- Familiarity with MLOps platforms like MLflow or Kubeflow
- Hands-on work with real-time data streams
- Background in e-commerce or retail domains
Compensation
Competitive salary and performance-based incentives commensurate with experience
Work Arrangement
Hybrid work model with flexibility based on team and role requirements
Team
Part of a cross-functional AI and data science team focused on personalization and customer experience
About the Team
- This role is part of a dedicated machine learning group building intelligent systems that enhance customer engagement through personalized content and product suggestions.
- The team works at the intersection of research and engineering, delivering scalable solutions that impact millions of users.
What You'll Achieve
- Drive the technical vision for recommendation engines across digital platforms.
- Deliver models that improve customer satisfaction and business outcomes through personalization.
May be available for qualified candidates, subject to business needs and policy
