Machine Learning Engineer, Etsy
Fulfillment
$140-210k
Plus equity package and an annual performance bonus. Salary dependent on location
More information about location
Office located in Brooklyn, New York
eCommerce platform for handcrafted goods
Posted today
eCommerce platform for handcrafted goods
1001+ employees
Posted today
$140-210k
Plus equity package and an annual performance bonus. Salary dependent on location
More information about location
Office located in Brooklyn, New York
1001+ employees
Company mission
To keep human connection at the heart of commerce.
Role
Who you 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
What the job involves
- 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
- 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?
- 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
Application process
- 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 take
Created as an alternative to more corporate eCommerce sites, Etsy is a peer-to-peer (P2P) website where users buy and sell handmade crafts, vintage items, art and photography. The online marketplace works similarly to Amazon and eBay, only with an emphasis on unique items rather than mass-produced merchandise.
Since its IPO in 2015, it has managed to hold its own in an increasingly competitive eCommerce industry, including from Amazon's handmade section that launched in 2015. Further, by providing sellers with ownership over pricing and branding, the site is attractive for those wanting to set up a business on an established platform.
However, the seller-side of Etsy is not without problems. A hike in transaction fees and the introduction of mandatory marketing fees for artists with high turnovers created significant discontent amongst sellers. Despite this, Etsy continues to connect artists and consumers, attracting shoppers with its message of extraordinary, handmade and affordable products.
Freddie
Company Specialist at Welcome to the Jungle
Insights
8% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
May 2012
$40m
LATE VC
Aug 2010
$20m
SERIES E
Total funding: $91.7m
Company benefits
- Equity (offers vary by country)
- Performance bonuses
- Paid gender neutral parental leave
- Adoption and family planning
- Back-up care
- Volunteer time off
- Donation matching
- Employee resource groups
- Global virtual events
- Employer paid health benefits
- Fitness benefits
- Wellness programming
- Mental health benefits
- 401K matching/Pension contributions
Company HQ
Downtown Brooklyn, NY
Leadership
Josh Silverman
(CEO)Previously senior executive at eBay, CEO of Shopping.com, CEO of Skype and President of Consumer Products & Services at American Express.
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