Staff ML Engineer

Part-time
Temporary
Volunteer
Brick & Morter
Description

Shopify is looking for an accomplished staff level software engineer to join our ML Platform Engineering team. In our world, we care deeply about the availability, performance, and stability of the data processing, storage engines and models that power Shopify’s many data products.  This isn’t a traditional Data Engineering role, you would not be building individual pipelines for analytics users, and is closer in scope to software engineering roles tied to web-scale platform development.     

Our team gets to shape the future of ML at Shopify, and support the company in making better decisions. As part of our work we build CI/CD pipelines, release libraries, scale compute clusters, integrate and monitor services, and design workflows that allow our data scientists to develop and productionize ML models.  

We’re looking to find passionate and diverse software developers who get excited about how data can empower the 1M+ merchants that use Shopify today. Apply now to join us.

You should have:

  • At least 5 – 10 years of experience as a software engineer
  • A variety of software development experience – you are proficient with things like software design patterns, code review, multiple languages and paradigms, TDD, etc.
  • Deep familiarity with common machine learning tools and libraries
  • Database experience – you are familiar with things like SQL query authoring, table design patterns for OLAP and OLTP, a variety of databases, and the tradeoffs between them
  • Experience with building and maintaining distributed systems, and knowledge of the associated patterns, concerns, and tradeoffs
  • Demonstrated ability to work in multiple languages and platforms
  • Experience taking the lead on high-impact projects

It’d be nice if you had experience with:

  • Python, Spark, Scala, Go, or Java
  • Cloud Computing (Google Cloud, AWS, Azure)
  • Dev ops (Docker, K8s, Terraform)
  • Optimized storage strategies such as columnar file formats, partitioning, bucketing, and bloom filters