Staff Applied Scientist I

Etsy•New York, NY

5d•Hybrid

About The Position

We’re looking for a Staff Applied Scientist I to join our Machine Learning Team for Growth. In this role, you’ll lead initiatives focused on leveraging machine learning to drive business growth across multiple channels—think messaging notifications, SEO, and paid advertising – anything in the realm of growing Etsy. You’ll be pioneering new solutions from the ground up, staying abreast of the latest research, and owning the end-to-end ML lifecycle—from data extraction to production deployment. This is a full-time position reporting to the Senior Engineering Manager, Growth ML. 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. For this role, we are considering candidates based in the United States. Candidates living within commutable distance of Etsy’s Brooklyn Office Hub or in the San Francisco Bay Area may be the first to be considered. For candidates within commutable distance, Etsy requires in-office attendance once or twice per week depending on your proximity to the office. Etsy offers different work modes to meet the variety of needs and preferences of ou r team. Learn more details about our work modes and workplace safety policies here. What’s this team like at Etsy? We’re scrappy and product-minded: we collaborate with teams who aren’t steeped in ML, and bring a clear ML vision that drives real business outcomes. We stay close to the latest research—not to chase novelty, but to find proven techniques we can apply to real problems and ship effective solutions fast We focus on taking projects from 0 to 1 in areas and product spaces where ML is just getting started—think message notifications/campaigns, paid ads optimization, and generally driving traffic to Etsy via ML solutions. With 100+ million users and billions of messages and ads sent each month, our work directly impacts Etsy’s growth at a massive scale. We value creative problem-solvers who are eager to tackle real-world challenges—backed by practical habits like code reviews, documentation, and thoughtful testing to help ideas scale.

Requirements

Proven track record in ML: You have 8+ years of experience building and deploying ML solutions. You’re familiar with modern ML frameworks (such as TensorFlow or Torch), distributed computing, and cloud environments.
Strong coding skills: You write robust, production-quality code (Python, Scala, Java, or equivalent). You’re experienced with tools like Spark, GCP, and Airflow.
Hands-on experience across the pipeline: From data extraction and preprocessing to training, evaluating, and deploying models in production environments.
Comfort in ambiguity: You excel in situations where the scope is not fully defined, proactively finding clarity and driving forward.
Collaboration and communication: You can articulate complex concepts to both technical and non-technical audiences and enjoy working on cross-functional teams.
You genuinely enjoy solving hard problems, improving real user experiences, and using modern tools to build smart—not needlessly complex—solutions.

Nice To Haves

Bigquery and Scala experience is a plus.
Experience in search or recommender systems: Preferred but not required. This could include ranking algorithms, personalized recommendations, or large-scale search optimization.
Advanced degree in ML or related field: Preferred but not required. We recognize relevant industry experience can be just as valuable.

Responsibilities

Take solutions from 0 to 1: You’ll be comfortable working in ambiguous problem spaces, experimenting, and driving projects from an initial idea to a fully operational ML product. You are comfortable proving out ideas quickly as well as establishing long term solutions.
Leverage existing ML infrastructure and partnerships with platform teams to build scalable and modular ML systems
Track cutting-edge ML research and bring back ideas we can actually use to build smarter, faster, and more impactful solutions. GenAI, Advanced RecSys, GraphML – all of it is on the table.
Productionize ML solutions: Develop robust systems for training, deploying, and monitoring ML models at scale.
Collaborate cross-functionally: Work closely with other disciplines like Data Engineers, Product Managers, Marketing, and other stakeholders to align projects with broader product goals.
ETL and data analysis: Build critical data pipelines, perform exploratory analysis, and ensure data quality and reliability – especially for model training data.
Experimentation and measurement: Design and execute A/B tests to measure the effectiveness of ML-driven features, iterating quickly based on findings.