Job Title: Machine Learning Manager,  Advertising Optimization & Automation Science

Who We Are

Wayfair’s Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We are building Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques. 

The Advertising Optimization & Automation Science team is central to this effort. We leverage machine learning and generative AI to streamline campaign workflows, delivering impactful recommendations on budget allocation, target Return on Ad Spend (tROAS), and SKU selection. Additionally, we are developing intelligent systems for creative optimization and exploring agentic frameworks to further simplify and enhance advertiser interactions.

We are seeking an experienced Machine Learning Manager to lead a growing team focused on building intelligent, ML-powered systems that drive personalized recommendations and campaign automation within Wayfair’s advertising platform. In this role, you will have the opportunity to define and deliver 0-to-1 capabilities that unlock substantial commercial value and directly enhance advertiser outcomes. ​Working closely with a high-performing team of ML scientists and engineers, you'll tackle some of Wayfair's most intellectually challenging problems in machine learning, latency, and scalability, while directly contributing to the company's bottom line.

What You’ll do

• Own the strategy, roadmap, and execution of supplier advertising intelligence and automation solutions. Lead the next phase of GenAI-powered creative optimization, ML-based recommendation systems, and campaign automation to drive significant incremental ad revenue and improve supplier outcomes.
• Build, coach, and manage a team of ML scientists focused on developing intelligent budget, tROAS, and SKU recommendations, creative testing frameworks, and simulation-driven decisioning that extends beyond the current platform capabilities.
• Partner cross-functionally with Product, Engineering, and Sales to deliver scalable ML solutions that improve supplier campaign performance. Ensure systems are designed for reuse, extensibility, and long-term impact across multiple advertising workflows.
• Research and apply best practices in advertising science, GenAI applications in creative personalization, and auction modeling. Keep Wayfair at the forefront of innovation in supplier marketing optimization.
• Collaborate with Engineering teams (AdTech, ML Platform, Campaign Management) to build and scale the infrastructure needed for automated, intelligent advertising decisioning.
• Act as SME and provide mentorship and technical guidance beyond own team on the broader DS/Eng org when needed

What You’ll need

• Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, or related field.
• 9+ years of industry experience, with at least 1-2 years experience as a manager of teams and 5+ serving as an IC on production ML systems.
• Strategic thinker with a customer-centric mindset and a desire for creative problem solving, looking to make a big impact in a growing organization 
• Demonstrated success influencing senior level stakeholders on strategic direction based on recommendations backed by in-depth analysis; Excellent written and verbal communication
• Ability to partner cross-functionally to own and shape technical roadmaps and the organizations required to drive them.
• Proficient in one or more programming languages, e.g. Python, Golang, Rust etc.

Nice to have

• Experience with GCP, Airflow, and containerization (Docker)
• Experience building scalable data processing pipelines with big data tools such as Hadoop, Hive, SQL, Spark, etc.
• Familiarity with Generative AI and agentic workflows
• Experience in Bayesian Learning, Multi-armed Bandits, or Reinforcement Learning