Director of Engineering, Machine Learning Recommendations

etsy; United States; On-site; USD 250,000 - 420,000; Full time

Job summary

Etsy is seeking a Director of Engineering, Machine Learning to lead the Recommendations team and its ML-driven on-site discovery across the marketplace. You will own end-to-end technical strategy, manage ML engineers, applied scientists, and system engineers across retrieval, ranking, and generation, and drive architecture toward generative, LLM-enhanced experiences.

Reporting to the VP of Engineering for Search, Recommendations, and Ads, you will mentor managers, define multi-year roadmaps, and

Qualifications

• 10+ years of progressive experience in ML engineering.
• Experience leading multi-disciplinary ML teams and managers.
• Shipping large-scale recommendation systems and data-driven products.

Responsibilities

• Define and drive the multi-year product and technical roadmap for Recommendations ML.
• Lead and grow a team of ML engineers, applied scientists, and platform engineers.
• Partner with product, UX, and cross-functional ML teams to align on an end-to-end buyer experience.

Skills

ML engineering

Team leadership

Product roadmap

Recs systems

Job description

Company Description

Etsy is the global marketplace for unique and creative goods. We build, power, and evolve the tools and technologies that connect millions of entrepreneurs with millions of buyers around the world. As an Etsy employee, you will tackle unique, meaningful, and large-scale problems alongside passionate coworkers, all the while making a rewarding impact and Keeping Commerce Human.

We believe exceptional companies are built by exceptional teams, and we're intentional about it. That means hiring great people, setting them up for success from day one, and giving them real reasons to grow their careers here. We invest in development that goes beyond promotions, and we foster the trust and relationships that help people do their best work together.

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, retr