Etsy is hiring a Machine Learning Engineer to join the Inventory ML team. We’re a cross-functional group of six machine learning scientists and engineers supported by product, design and analytics partners. Our team develops custom machine-learning models to improve the shopping experience for millions of Etsy buyers and the selling experience for millions of etsy sellers.

About The Role

You will be responsible for contributing to the team’s road map and our daily processes, specifically:

Attendance at daily stand ups, backlog refinement meetings, retrospectives, sprint kickoff, project meetings and 1:1s.
Breaking work down into manageable chunks that you and your teammates can execute on.
Delivering well scoped work in a timely manner. Work should be discussed with the whole squad in our backlog refinement meetings.
Applying engineering best practices: PR reviews, unit and integration testing, monitoring, etc.
Collaborating closely with our product, design and analytics partners to shape the definition of your project and iterating as the project evolves.
Helping to come up with ideas to do low-fidelity and medium-fidelity testing/validating
Helping to triage and fix bugs, including shifts on our on-call rotation
Reviewing code for other engineers on the team
Proactively providing status of ongoing work and communicating about technical challenges as they are anticipated or arise
Consulting on technical feasibility and level of effort for ideas

Requirements

Must-Haves

Proficient with standard machine learning tools and practices, such as fine-tuning, evaluation, model deployment, iteration, monitoring, debugging.
Experience with GCP tools (or equivalent): BigQuery, Vertex AI, Gemini, Dataflow.
Ability to collaborate and communicate effectively with teammates and cross-functional partners.

Nice-to-Haves

Expertise in fields such as natural language processing, computer vision, reinforcement learning, deep learning, multimodal learning, or large language models
Experience with modern approaches to AI development and deployment: context engineering, fine-tuning open source LLMs, cost and throughput management, vLLM, GPU management, etc.
Experience developing, scaling and maintaining real-time ML/AI inference systems.
Interview process
30-min manager screen, followed by a 60 min technical assessment (e.g. live coding session or technical deep dive) with 1-2 members of the hiring team.