• Lead and mentor a high-performing Machine Learning Science team focused on delivering impactful ML/AI solutions that drive business outcomes. • Partner closely with cross-functional engineering and product teams to define, prioritize, and execute high-impact initiatives. • Drive research and development of machine learning models, including semantic search, personalized and real-time recommendations, and agentic learning approaches. • Define and execute a mid- and long-term vision for scalable ML architecture across key domains such as personalization, search, and real-time performance optimization. • Recruit, develop, and retain a diverse team of top-tier scientific talent, elevating the capabilities and influence of The Knot’s Machine Learning Science organization. • Actively contribute to the broader technical community by sharing insights through internal knowledge-sharing sessions, technical documentation, and external publications or blog posts.

• Partner with product, UX, and technical stakeholders to analyze business problems, clarify requirements, define scope, and translate them into measurable ML problem statements. • Design, implement, and maintain scalable, enterprise-grade ML solutions in production. • Build reproducible ML workflows for data preparation, training, evaluation, and inference using modern orchestration and MLOps tooling. • Implement monitoring and evaluation frameworks to continuously improve data quality, model performance, latency, and cost through feedback loops. • Partner cross-functionally with Product, Data Science/ML, Engineering, and Security to deliver resilient, scalable, and compliant ML-powered services. • Demonstrate end-to-end systems understanding and articulate the “why” behind model and system design choices. • Own operational excellence: SLAs, on-call, incident response, customer feedback triage, and blameless post-mortems. • Drive engineering excellence via AI-assisted SDLC, code reviews, automated testing, MLOps best practices, knowledge-sharing, and mentoring. • Actively adopt AI-assisted practices to improve implementation and collaboration efficiency.

• Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability. • Tackle complex challenges associated with resource-intensive models using distributed systems and parallel computing. • Advance methodologies for controlling, monitoring, and analyzing machine learning models in production environments. • Develop new approaches to adversarial testing, model evaluation, and robust inference. • Translate research ideas into scalable AI systems deployed in real-world, adversarial settings. • Mentor junior engineers and drive innovation within the team. • Work closely with cross-functional teams to ensure research outcomes inform production systems.

• Own Model Development: Lead the end-to-end design, development, and deployment of advanced ranking algorithms and retrieval models to deliver highly relevant search results and content. • Innovate in Semantic Search: Fine-tune and distill Language Models to enhance their domain-specific expertise. You'll implement cutting-edge semantic embedding techniques (e.g., binary, matryoshka, late interaction models) to optimize for speed and accuracy. • Build & Scale Recommender Systems: Architect and implement sophisticated recommender systems that personalize the user experience, driving engagement and content discovery. • Collaborate & Mentor: Work closely with product managers, software engineers, and data scientists to translate business needs into technical solutions. • Stay Ahead of the Curve: Actively research and evaluate emerging trends and academic research in ML, NLP, and Information Retrieval, prototyping new ideas to maintain our competitive edge.