Senior or Staff (Lead) MLOps Engineer
Opportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. Since 2006, we’ve grown to over 10,000 employees and generated over $500 billion in sales for millions of merchants in 175 countries. Every 28 seconds, an entrepreneur on Shopify makes their first sale.
This is life-defining work that directly impacts people’s lives as much as it transforms your own. This is putting the power of the few in the hands of the many, is a future with more voices rather than fewer, and is creating more choices instead of an elite option.
Moving at our pace brings a lot of change, complexity, and ambiguity—and a little bit of chaos. Shopifolk thrive on that and are comfortable being uncomfortable. That means Shopify is not the right place for everyone.
Before you apply, consider if you can:
- Care deeply about what you do and about making commerce better for everyone
- Excel by seeking professional and personal hypergrowth
- Keep up with an unrelenting pace (the week, not the quarter)
- Be resilient and resourceful in face of ambiguity and thrive on (rather than endure) change
- Bring critical thought and opinion — and embrace differences and disagreement to get shit done and move forward
- Work digital-first for your daily work
As an MLOps at Shopify, your primary responsibility is to build, maintain and constantly improve ML-powered solutions to drive better outcomes for Shopify users. In Commerce Trust and Integrity (CT&I), specifically, one of our largest responsibilities is preventing bad actors from using our platform to defraud buyers and Shopify. However, we also try to keep our platform from being misused and identify merchants that are struggling both to lend a helping hand and protect Shopify from exposure to credit losses. The only way to do this at our scale is utilizing machine learning models for detection and in some cases automation. Data science is absolutely critical to the continued success of CT&I.
Fraud detection and credit risk assessment require fast responses and evolving strategies as our opponents never cease to find new methods of attack. In this role, you’ll be joining a team of motivated and engaged data scientists constantly iterating on models and data pipelines as we try to keep up with those intent on using our platform for ill. We must prioritize getting shit done, shipping fast, and constantly learning. Inquisitiveness and an ability to adapt will help you thrive.
You will be managing our machine learning lifecycle and operations using Google Cloud Platform, Vertex AI and Docker to scale our data and model inference pipelines so we can build robust ML services. We want to catch fraudsters as quickly as possible to mitigate damage and you will be central in trying to reduce the latency of our model predictions and stand-up realtime inference pipelines. It is an exciting time here as we have our baseline defences up and are looking towards a future of state-of-the-art models and ML operations practices.
- Professional experience in a data science or engineering role applying ML to solve business problems.
- Experience with best-in-class ML operations to monitor and maintain models in production and ensure top quality, stable predictions with high uptime.
- Proven track record of deploying models on Google’s Vertex AI ML platform and GCP or equivalent services like Amazon SageMaker or Azure ML.
- Hands-on utilization of containers (Docker) and orchestration technologies for development and deployment of machine learning models.
- Built complex and optimized data pipelines and driven important ETL design decisions for an organization.
- Ability to communicate effectively with technical and non-technical stakeholders.
- Past incidents of upskilling data scientists and ML engineers in best practices for data pipelines, coding practices and machine learning deployments.
It would be great if you have:
- Expert knowledge in architecting and scaling distributed processing systems (like Spark or Dask) for high performance computing to support model training and inference.
- Deployed and managed a model that provided realtime inference for making critical and immediate business decisions.
- Previous usage of GitOps technologies, particularly Terraform
- Demonstrated examples of moving the state of the art on ML algorithms in applied contexts (solve a clear product problem and then publish/patent)
We hire people, not resumes. If you think you’re right for the role, apply now. Please submit your CV/Resume in PDF format.
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