Senior Software Engineer I, Machine Learning, ML Enablement

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? We are looking for a Senior Software Engineer, Machine Learning to help build and operate the infrastructure that trains, hosts, and serves Etsy's machine learning models — including our internal serving platform, and a growing portfolio of predictive and generative (open source LLM) models. You won't just be deploying models; you'll be architecting the systems that make model serving fast, reliable, and scalable for millions of inferences per second. Your work directly enables Applied Scientists and Engineers across Etsy to train, host, and iterate on models with confidence, from classic ML predictors to large open-source language models. This is a full-time position in ML Enablement team, reporting to its Engineering Manager. What’s this team like at Etsy? The Machine Learning Enablement initiative builds the core infrastructure that turns complex ML workflows into seamless, self-service platforms for Etsy’s Applied Scientists and Engineers. The Models Training and Serving Platform team builds and operates the core infrastructure that powers model training and serving across Etsy, including Barista, our internal model-serving platform, along with a range of hosted modelsets and open-source LLMs. We own the path from a trained model to a production-ready, observable, and scalable serving endpoint — spanning infrastructure, deployment tooling, and reliability. We work on significant, complex challenges that intersect multiple critical teams and systems, where you can make a rewarding impact. We are light on process and heavy on collaboration, working with many partner teams within our org and beyond in order to improve our leverage. We are a platform team with a product driven mentality, driving innovation using our Machine Learning systems in effective and creative ways. What does the day-to-day look like? Write high-quality, production-grade Python code (additional languages a plus); participate in code reviews and pair programming; contribute to and help drive architectural decisions. Build, operate, and improve infrastructure for training and serving ML models on GCP and Kubernetes, with a focus on scalability, reliability, and observability. Design and maintain serving infrastructure for open-source and internally hosted LLMs, including model deployment, resource management, and performance tuning. Apply working knowledge of ML fundamentals — including neural network deep learning as well as latest transformer architectures along with prediction and inference systems — to make sound infrastructure and design decisions. Partner cross-functionally with Applied Scientists to understand model training and serving needs, and translate that understanding into infrastructure that removes friction from their workflows. Contribute to system design discussions, weighing tradeoffs across performance, cost, and reliability for infrastructure serving 100M+ users. Thoughtfully use generative AI and other productivity tools to work efficiently, with a focus on learning and intentional contribution. 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