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Open nowPosted 46 days ago

Machine Learning Engineer II, Fulfillment

Etsy73 open roles

Pay
$156,000 – $210,000 a year
Where
Brooklyn, New York
Work mode
Hybrid
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Your applicationOpen nowMachine Learning Engineer II, FulfillmentEtsy · Brooklyn, New York
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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. Etsy postings stay open a median of 14 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 46 days ago

Etsy median: 14 days open

The posting

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?

Etsy is hiring a Machine Learning Engineer II to join the Fulfillment ML team. Our team owns the machine learning that powers Etsy's shipping and delivery experience - models that help buyers see accurate delivery estimates, help sellers price and ship their orders accurately, and help Etsy make smart fulfillment decisions at scale. Examples of models we own include Estimated Delivery Date (EDD) prediction, shipping price prediction, transit-time modeling, and delivery-risk signals. We're a multi-functional group of engineers, applied scientists, and product managers, and we partner closely with data scientists, designers, and product-facing engineering teams across Etsy.

This role combines ML platform engineering with hands-on applied ML work. On the platform side, you'll build and operate the pipelines, orchestration, and tooling that take our models from training through validation, promotion, and inference in production. On the applied side, you'll partner with senior teammates on model evaluation, backtests, tuning, and readiness reviews - you're not expected to design new model architectures yourself, but you will work deeply with the models, learn how they behave, and grow your applied ML expertise over time.

This is a great opportunity for a strong software engineer early in their ML career who wants to level up on both the systems side and the modeling side, inside a mature MLOps stack that ships model predictions to millions of Etsy buyers and sellers every day.

This is a full-time position reporting to the Engineering Manager, Fulfillment ML.

What's this team like at Etsy?

Fulfillment ML owns the machine learning behind Etsy's shipping and delivery experience - across buyer-facing surfaces like Search, Listing, Cart, and Checkout, and across seller-facing tools. Our team includes MLEs, applied scientists, and platform engineers. New model design is usually led by senior teammates; the surrounding applied ML work (evaluation, backtests, tuning, readiness) and the platform/infrastructure work are shared responsibilities across the team, including this role. Our stack includes Python, PyTorch, Ray, Spark, Airflow, Google Cloud's Gemini Enterprise Agent Platform (formerly Vertex AI), Dataproc, BigQuery, and GCS.

Here's a taste of the problems we're solving:

  • How do we predict when an order will arrive at a buyer's door across dozens of countries, carriers, and shipping methods?
  • How do we tell buyers what's likely to arrive by a given date without underpromising or overpromising?
  • How do we spot delivery risk early enough to help sellers and buyers make good decisions?
  • How do we retrain, validate, and promote models weekly without breaking anything downstream?
  • How do we notice a model is drifting before buyers or sellers do?
  • How do we roll back a bad model in minutes, not hours, with confidence?
  • How do we predict shipping costs so buyers know what to expect at checkout and sellers can plan fulfillment with confidence?
  • How do we make our shipping and fulfillment signals feel consistent everywhere they show up in the buyer journey?

What does the day-to-day look like?

  • Build and maintain the training, validation, promotion, and batch-inference pipelines that put our models into production and keep them healthy there.
  • Write and extend Airflow DAGs within our templatized MLOps framework - training, inference, and validation workflows across staging, shadow, and production environments.
  • Partner with teammates on the applied ML side - running backtests, tuning model outputs to hit business targets (on-time delivery rate, cost, accuracy), and supporting seasonal readiness reviews.
  • Contribute to model migrations onto our internal ML platform - wiring up model configs, feature pipelines, postprocessors, and inference runners alongside the teammates who design the models.
  • Triage and debug production incidents that affect model-driven surfaces (for example, delivery estimates on Cart/Checkout or shipping prices in Search results), and coordinate fixes with partner teams.
  • Improve our observability - dashboards, retrain trending, data-quality tags, and alert hygiene - so problems are easier to spot before they reach buyers or sellers.
  • Write clear PRs, small-medium design docs, and runbooks so what you learn becomes durable for teammates.
  • Take part in the team's on-call rotation for model-health and data-quality alerts. You'll ramp into on-call gradually with senior support and clear runbooks.
  • 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 in this role are:

  • You collaborate and communicate effectively with teammates and multi-functional partners.
  • You have 1-3 years of professional experience building and shipping production ML systems (internships and academic research projects that shipped can count), with strong Python skills and a good sense for code hygiene, testing, and reviewing your own work before it lands.
  • You have some exposure to batch data pipelines, ML orchestration (Airflow, Kubeflow, Dagster, Prefect, or similar), and you're comfortable with SQL and at least one cloud data warehouse (BigQuery, Snowflake, Redshift) - or ready to ramp quickly.
  • You understand the ML lifecycle well enough to reason about model behavior with teammates - training data, evaluation metrics, validation splits, and what it takes to put a model into production. You don't need to be able to design a new model architecture on your own.
  • You produce clear, testable, and maintainable code, and you're eager to be mentored, receptive to feedback, and comfortable growing inside an established codebase.
  • You are mindful of the impact your work has on Etsy buyers and sellers.
  • Bonus: You have prior experience with PyTorch or another deep learning framework; exposure to distributed compute (Spark, Ray, or Dask); experience contributing to orchestration or feature pipelines
  • Bonus: Prior experience in e-commerce, shipping, or logistics problem spaces.

Compensation & Benefits

In addition to salary, you'll be eligible for an equity package, an annual performance bonus, and our competitive benefits 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 here.

Salary Range:

• Geo-1: 156,000.00 - 210,000.00 USD Annual • Geo-2: 140,000.00 - 190,000.00 USD Annual

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.

Our Promise

At Etsy, we believe that a diverse, equitable, and inclusive workplace furthers relevance, resilience, and longevity. We encourage people from all backgrounds, ages, abilities, and experiences to apply. Etsy is proud to be an equal opportunity workplace and is committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, national origin, religion or creed, sex, pregnancy (including childbirth and related medical conditions), gender, gender identity or expression, sexual orientation, age, citizenship or immigration status, marital or familial status, physical or mental disability, medical condition, genetic information, military or veteran status**, or any other characteristic protected by applicable federal, state, or local law**. If, due to a disability, you need an accommodation during any part of the interview or hiring process, please let your recruiter know and we will work with you to meet your needs. While Etsy supports visa sponsorship, sponsorship opportunities may be limited to certain roles and skills.

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