As Staff Engineer I you will be responsible for building and scaling our machine learning infrastructure at Etsy. We are working on cutting-edge foundational tools that accelerate model prototyping and development by generating datasets for model training with point-in-time accuracy, ensuring minimal training-serving skew. It's an integral part of our ML Enablement efforts, enhancing the efficiency and accuracy of our data-driven solutions, that will support various high visibility Machine Learning teams at Etsy including Search Ranking, Personalized Recommendations, etc.

Do you find joy in sophisticated architectures that involve distributed systems, machine learning and multiple collaborators? If so, this could be the perfect match.

This is a full-time position reporting to the Senior Engineering Manager, ML Systems. 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 our team. Learn more details about our work modes and workplace safety policies here.

What's this team like at Etsy?

The Features team is part of ML enablement organization. Our mission is to enable efficient and impactful machine learning across Etsy as a platform.

What does the day-to-day look like?

• Be a part of a team that will build and maintain foundational ML infrastructure tools for generating training datasets for model training with point-in-time accuracy that will support various high visibility Machine Learning teams at Etsy including Search Ranking, Personalized Recommendations, etc.

• Build high scale Spark & Dataflow jobs that interact with feature systems and model training. This includes designing and architecting large-scale distributed systems and ML systems, and building distributed services with technologies such as Docker, Kubernetes, gRPC, Spark, etc.

• Develop and maintain distributed infrastructure with a deep understanding of batch distributed computation engines and columnar file storage optimization techniques to speed up feature retrieval and training. 

• Coach and train junior team members.

• Actively work with leadership and product teams on the roadmap and new features.

• Lead and coordinate cross-team projects across the organization and actively promote best practices and industry standards to maintain the high quality of the product and services.

• 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.

• Infrastructure background with 10+ years of industry experience (with at least 1-2 years of experience as a Tech lead) working on distributed systems, ML infrastructure and other high scale applications. 

• You have a background in Computer Science and related fields, equivalent to a B.S. degree or above in Computer Science, Electrical Engineering, Information Technology, or related engineering fields.

• You understand that a core component of the Staff Engineering role is to be a force multiplier, leveling up the contributions and thinking of the engineers around you.

• You have practical experience in designing and architecting large-scale distributed systems and machine learning infrastructure.

• You demonstrate strong engineering and coding skills, with the ability to write high-performance production code. Proficiency in Scala is required and experience with highly concurrent frameworks (e.g. Cats effects) is required.

• You possess strong proficiency in creating and optimizing large-scale Spark & Dataflow pipelines, as well as general data infrastructure technologies. This includes the ability to optimize storage formats and reduce job runtime & costs.

• You have experience in building distributed services with technologies such as Docker, Kubernetes, gRPC, Spark, and are familiar with concurrency frameworks and performance tuning that build low latency applications.

• Experience with streaming data and its application to ML solutions is a plus.