Director of Engineering, Machine Learning Recommendations

Etsy•Brooklyn, NY

9d•Hybrid

About The Position

We are looking for a Director of Engineering, Machine Learning to lead Etsy's Recommendations, the team responsible for the algorithms and systems that power personalized onsite-discovery across our marketplace. This is a pivotal technical leadership role at the intersection of ML research, systems engineering, and product impact. In this role, you will own the end-to-end technical strategy for how Etsy connects buyers with the right listings across the App Homefeed, Web Home, Shop Home, Listing pages, Cart, and Checkout pages. You will lead a team of ML engineers, applied scientists, and system engineers organized across three areas: candidate retrieval, ranking, and recommendation systems. You will drive the evolution of our models from state-of-the-art multi-interest retrieval and multi-task deep ranking to next-generation LLM-powered foresight recommendations and generative discovery experiences. The biggest unsolved challenge in front of this team is not just relevance — it is inspiration: helping buyers discover new ideas and start new shopping missions. This opportunity is a full-time position reporting to the VP of Engineering at Search, Recommendations, and Ads.

Requirements

Proven ability to lead and develop multi-disciplinary teams of ML engineers, applied scientists, and platform engineers, including managing managers and senior managers.
10+ years of progressive experience in ML engineering, with demonstrated success shipping large-scale recommendation, retrieval, or ranking systems into production.
A track record of driving complex, cross-functional technical programs to completion — including navigating shared infrastructure decisions with partner teams in Search, Ads, or adjacent ML platforms.
Sharp instincts for balancing near-term product impact with mid-to-long-term research investment, and the ability to communicate that trade-off clearly to both engineering and product leadership.
Familiarity with large-scale ML infrastructure: distributed training (TensorFlow, PyTorch), feature stores, model serving, and online experimentation frameworks.

Responsibilities

Define and drive the multi-year product and technical roadmap for Recommendations ML, with particular focus on personalization, foresight, diversity, and content freshness — the four problems where we have the most room to grow.
Lead and grow a team of ML engineers, applied scientists, and platform engineers across retrieval, ranking, and systems sub-teams, each with its own engineering manager.
Partner closely with product, UX, and cross-functional ML teams (Search, Ads, Buyer Understanding, Content Understanding) to align on a coherent, end-to-end buyer experience and shared infrastructure investments.
Drive the architectural evolution from our current two-stage retrieval-and-ranking pipeline toward more generative and LLM-integrated recommendation architectures.
Set the technical bar for model development, experimentation, evaluation, and productionization — including our offline VQA and LLM-as-judge evaluation frameworks.
Champion a high-velocity experimentation culture: our Multi-Variant Dataset (MVD) framework allows parallel A/B tests across retrieval and ranking components, and you should know how to squeeze value out of every experiment.
Mentor and develop engineering talent at all levels, including Staff and Senior Staff engineers and the engineering managers who report to you.