Staff Product Manager, ML Enablement

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 Inc. employee, whether a team member of Etsy or Depop, you will tackle unique, meaningful, and large-scale problems alongside passionate coworkers, all the while making a rewarding impact and Keeping Commerce Human.

Salary Range:

$194,000.00 - $252,000.00

What’s the role?

We are looking for a Staff Product Manager to join our Machine Learning Enablement (MLE) organization. MLE builds the tools, platforms and developer experiences that power every machine learning system at Etsy enabling hundreds of scientists and engineers to train, evaluate and deploy models that deliver value across Search, Ads and Recommendations.

As a Staff PM on this team, you’ll help build the next generation of Etsy’s ML platform: enabling faster ML developer velocity, improved reliability and more cohesive developer experience ensuring our platform investments deliver measurable impact for ML practitioners.

This is a full-time position reporting to the Group Product Manager and the base salary range will be 194,000 - 252,000 USD per year. In addition to salary, you will also be eligible for an equity package, an annual performance bonus, and our competitive benefits that support you and your family as part of your total rewards package at Etsy.

This role requires your presence in Etsy’s Brooklyn office in an in-person or flex capacity. Candidates living within commutable distance of the Brooklyn Hub, may be the first to be considered.

Etsy offers different work modes to meet the variety of needs and preferences of our team. Learn more about our Flex and Office-based work modes and workplace safety policies here.

What’s this team like at Etsy?

The ML Enablement initiative owns the systems and infrastructure that support ML ecosystem end-to-end. Teams within MLE focus on:

• Feature and embeddings infrastructure: Delivering reliable, consistent, high-quality feature and embedding data for training and serving ML models.

• Model development and training: Building standardized PyTorch development and training workflows that enable fast iteration for ML practitioners.

• Orchestration and integration testing: Powering online experimentation and offline evaluation, model deployment and developer velocity.

This role will flex across these problem spaces depending on business needs giving you the opportunity to define strategy and deliver impact across multiple high-leverage areas.

What does the day-to-day look like?

• Product Strategy: Define the strategy and roadmap for a cohesive ML development experience at Etsy, ensuring consistency across data, training and serving.

• Product Execution: Partner with engineering leads to simplify ML workflows and reduce friction in model development and deployment.

• Product Collaboration: Collaborate with ML product teams to understand their problems and prioritize platform investments that maximize impact.

• Quality Craft: Translate technical opportunities into clear customer outcomes and measurable success metrics.

• Product Leadership: Represent ML Enablement in cross-org planning with teams in Search, Ads, Recs, Data Enablement and Experimentation.

• Of course, this is just a sample of the work this role will require! You should assume that your role will encompass other tasks as needed and that your job duties and responsibilities may evolve over time.

Qualities that will help you thrive in this role

• Technical fluency and 5+ years of product management experience, with at least 2–3 years in ML, data or infrastructure-focused platform teams (e.g. model training and inferencing, feature stores, orchestration frameworks).

• Proven track record of working effectively with engineering in defining strategy and drive execution in complex, cross-functional technical spaces.

• Strong communication and collaboration skills to align stakeholders across engineering, data science and in