Example scenarios:

• "Implement an algorithm to merge overlapping search category intervals efficiently."
• "Write a SQL query to find the top-performing sellers in each category over the last 30 days, filtering out potential fraudulent transactions."

ML System Design & Scalability

This is a critical round where you will design an end-to-end machine learning system from scratch. You must demonstrate an understanding of how to scale models to handle Etsy's massive volume of traffic and listings.

Be ready to go over:

• Data Pipelines & Feature Stores – How to ingest, process, and store features for both real-time and batch prediction.
• Candidate Generation & Ranking – Designing multi-stage retrieval systems (e.g., vector search followed by heavy deep learning re-ranking models).
• Model Serving & Latency – Balancing model complexity with strict latency budgets (e.g., under 50ms for search results).

Example scenarios:

• "Design an image-based search system that allows users to upload a photo and find visually similar items on Etsy."
• "How would you build a real-time recommendation system that updates based on a user's in-session clicks?"

Technical Deep Dive & Presentation

During this round, you will present a past project in detail to a panel of scientists and engineers. This is your opportunity to showcase your deep expertise, technical leadership, and communication skills.

Be ready to go over:

• Problem Formulation – How you defined the business problem and translated it into a machine learning task.
• Technical Execution – The specific models, architectures, and data strategies you chose, and why.
• Business Impact – How you measured success, the results of online A/B testing, and the ultimate impact on the product.

Example scenarios:

• "Walk us through a project where you had to make a significant trade-off between model accuracy and system latency."
• "Describe a project where the offline evaluation results did not align with online A/B test results, and how you diagnosed the discrepancy."

As a Research Scientist at Etsy, your day-to-day work will span the entire lifecycle of machine learning development, from academic research to production deployment. You will be expected to:

• Conduct Applied Research: Stay at the forefront of machine learning, deep learning, and NLP research. Identify state-of-the-art techniques that can be applied to Etsy's unique marketplace challenges, such as search relevance, personalization, and catalog understanding.
• Build and Deploy Models: Design, train, and deploy scalable machine learning models. You will write clean, production-grade code to integrate these models into Etsy's core infrastructure, collaborating closely with software and platform engineers.
• Drive Product Impact: Partner with product managers to translate business goals into machine learning roadmaps. You will design and execute online A/B tests to validate the real-world impact of your models on user engagement, conversion rates, and seller success.
• Collaborate Cross-Functionally: Work in highly collaborative, cross-functional squads. You will act as a technical advisor, helping product and engineering teams understand the capabilities and limitations of machine learning.
• Mentor and Share Knowledge: Contribute to Etsy's strong engineering and research culture. You will mentor junior scientists, participate in internal peer reviews, and represent Etsy in the broader scientific community through publications and presentations.

We look for candidates who possess a strong blend of theoretical depth, practical engineering capability, and collaborative spirit.

Technical Requirements

• Educational Background: A Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a highly quantitative field, or equivalent practical industry experience.
• Programming Proficiency: Strong coding skills in Python, Scala, or Java, with a deep understanding of data structures, algorithms, and software engineering best practices.
• ML Frameworks: Hands-on experience with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or Scikit-Learn.
• Big Data Tools: Experience processing large-scale datasets using tools like Spark, Hadoop, SQL, and cloud-based data warehouses.

Experience & Soft Skills

• Industry Experience: Typically 3+ years of experience building and deploying machine learning models in a production environment, preferably in e-commerce, search, or recommendation systems.
• Communication Skills: The ability to explain complex technical concepts clearly to both technical and non-technical stakeholders.
• Collaboration: A proven track record of working effectively in cross-functional teams and driving projects to completion.

Preferred Qualifications

• Publications: A record of scientific publications in top-tier machine learning or NLP conferences (e.g., NeurIPS, KDD, SIGIR, RecSys, ACL).
• Search & Recommendation Expertise: Deep knowledge of learning-to-rank, vector databases, collaborative filtering, or graph neural networks.

Q: How technical is the coding round for Research Scientists? A: Etsy expects its research scientists to be strong coders. The coding rounds typically feature medium-level algorithmic questions. You should be comfortable writing clean, optimal code and discussing space and time complexity.

Q: What is the balance between research and engineering in this role? A: The role is highly applied. While you will spend time researching and designing novel architectures, a significant portion of your time will be spent writing production code, building pipelines, and running A/B tests. We value scientists who can write clean code and deploy their own models.

Q: How does Etsy evaluate culture fit? A: We evaluate this through our behavioral interviews, focusing on our core values: minimizing our environmental footprint, fostering a diverse and inclusive marketplace, and working with empathy and curiosity. We look for collaborative team players who care about the impact of their work on our community.

Q: What is the typical timeline for the interview process? A: The entire process usually takes between 3 to 6 weeks from the initial recruiter screen to the final offer decision. However, scheduling and team matching can sometimes extend this timeline.

Q: Is this role open to remote work? A: Etsy supports a hybrid working model with offices in locations like New York and Dublin, but we also offer fully remote options for many of our engineering and research roles depending on the specific team and location.

• Structure Your Answers: When answering behavioral and system design questions, use structured frameworks like STAR (Situation, Task, Action, Result). This keeps your answers concise and ensures you highlight your personal impact.
• Emphasize Scale: Throughout your interviews, keep scale in mind. Always explain how your proposed models or systems will scale to handle millions of listings and real-time traffic constraints.
• Showcase Product Thinking: Don't just focus on the math. Explain why a particular model makes sense for Etsy's buyers and sellers, and how you would measure its success using business-aligned metrics.
• Be Ready for Ambiguity: Etsy's data is highly unstructured. Show that you are comfortable working with noisy, incomplete data and can design robust systems that handle edge cases gracefully.
• Ask Thoughtful Questions: At the end of each interview, use the time to ask questions that show your genuine interest in the team's specific challenges, culture, and future roadmap.

Becoming a Research Scientist at Etsy offers a unique opportunity to apply cutting-edge machine learning to a highly expressive, creative, and global marketplace. The role demands a unique combination of deep scientific curiosity, strong engineering fundamentals, and a product-focused mindset. By preparing thoroughly across machine learning theory, coding, and scalable system design, you can position yourself for a highly successful interview loop.

Focus your preparation on mastering the fundamentals of search and recommendation systems, refining your algorithmic coding, and structuring your past technical achievements into compelling narratives. Approach your interviews with a collaborative spirit and a clear focus on user-centric problem solving.

For additional real-world interview insights, detailed company reviews, and comprehensive preparation resources from successful candidates, explore the tools available on Dataford.

The salary data shown above represents the typical compensation structure for this role at Etsy. When evaluating an offer, consider the full package, which includes a competitive base salary, equity components, and a robust benefits package designed to support your personal and professional well-being. Use this data to guide your expectations and conversations with your recruiter.