Principal Applied Scientist, Rokt

• PhD or Master’s in Computer Science, Statistics, Mathematics, or related field with specialization in ML, AI, or Information Retrieval
• 10+ years of industry experience building production-grade ML systems
• Deep expertise in at least two:
• Reinforcement Learning (Bandits, Policy Optimization)
• Multi-Task Learning & Mixture of Experts (MMOE, PLE, DCN)
• Deep Learning Architectures (Transformers, Graph Neural Networks)
• Bayesian Methods & Probabilistic Modeling
• ML for Ads, E-commerce, or Two-Sided Marketplaces (big plus!)
• Proven ability to drive impact across multiple teams and organizations

Desirable

• As a Principal Applied Scientist / Machine Learning Engineer, you bring deep expertise in machine learning and a strong track record of driving impact
• You will work closely with engineering leaders and senior stakeholders to tackle complex relevancy challenges, taking projects from research to full-scale deployment
• Your work will focus on designing, developing, and deploying proprietary machine learning models that power key business initiatives
• In this role, you will lead cutting-edge ML research and production deployment of high-impact models, advancing recommender systems, conversion optimization, user experience enhancement, and multi-task learning architectures
• This is a hands-on, individual contributor role for a seasoned expert who thrives in solving complex ML problems at scale
• You will design and implement advanced ML architectures, ensuring they are robust, efficient, and impactful in a fast-paced, high-performance environment
• Develop and implement machine learning models to solve high-impact business problems
• Design and optimize scalable ML pipelines and infrastructure for production environments
• Conduct applied ML research, prototype new modeling approaches, and rigorously test innovations
• Evaluate and improve model performance, ensuring robustness, scalability, and interpretability
• Translate complex business needs into practical ML solutions, collaborating with product and engineering teams
• Stay ahead of emerging ML trends, contributing to knowledge sharing through tech talks, brown bags, and best practice evangelism