What's the role?

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.

What's this team like at Etsy?

The Recommendations organization sits at the heart of Etsy's marketplace. We own over 60 recommendation modules across Etsy's surfaces, with our most impactful work powering the App Homefeed, Web Home, Shop Home, Listing pages, Cart, and Checkout pages. 

Our ML stack spans the full recommendation pipeline. On retrieval, we build and evolve our multi-interest user-to-listing embedding model as well as a LLM-powered system that generates forward-looking shopping ideas based on buyer personas. On ranking, we operate a multi-task deep ranking model jointly optimizing for clicks, favorites, ads-to-cart, and purchases. Underpinning all of this is ReactorRecs, our in-house serving and orchestration platform that composes retrieval, ranking, filtering, and experimentation across 200+ recommendation modules.

We believe in research grounded in real user impact. We run rigorous offline and online experiments, use LLM-based and human-in-the-loop evaluation, and hold ourselves to both engagement and discovery metrics.

What does the day-to-day look like?

• 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.

• Of course, this is just a sample of the kinds of work this role will require! You should assume that your role will encompass other tasks, too, and that your job duties and responsibilities may change from time to time at Etsy's discretion, or otherwise applicable with local law.

Qualities that will help you thrive:

• 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.

Compensation & Benefits

In addition to salary, you'll be eligible for an equity package, an annual performance bonus, and our that support you and your family. Base salary is determined by your location, skills, and experience.

Etsy uses two geographic pay zones to reflect the labor markets where we compete for talent: one called Geo-1, covering the New York City metropolitan area, San Francisco Bay Area, and Seattle metropolitan area, and one called Geo-2, covering all other U.S. locations. Salary ranges for this role vary by zone and are listed below.

Location

We prioritize candidates based within commutable distance of Etsy’s Brooklyn Office. Depending on proximity, we ask team members to come in once or twice per week. Remote candidates outside commutable distance may be considered on a case-by-case basis, with the expectation of occasional travel to the office. Learn more details about our work modes and workplace safety policies .

Salary Range:

What's Next

If you're interested in joining the team at Etsy, please share your resume with us and feel free to include a cover letter if you'd like. As we hope you've seen already, Etsy is a place that values individuality and variety. We don't want you to be like everyone else -- we want you to be like you! So tell us what you're all about.