This role can be performed remotely anywhere within the United States and Canada.

We are looking for a Machine Learning Engineer to join our growing efforts in data science, machine learning and R&D. Reporting to our Senior Director of Data Science, you’ll contribute to our efforts in Machine Learning Operations (MLOps) among our Data Intelligence group. Working with our Data Scientists, Machine Learning and Software Engineers, you’ll architect data pipelines, build our production machine learning infrastructure, and support machine learning research.

Day-to-day you’ll:

• Act as a subject matter expert in MLOps and Machine Learning Engineering.
• Help develop infrastructure and process for rapid machine learning feature experimentation, prototyping, deployment and evaluation with customers.
• Build and optimize data lakes, warehouses and features stores to feed research projects.
• Apply best practices for ETL, batch and stream processing to be leveraged for edata exploration and machine learning research.
• Serve as the bridge between the data scientists primarily creating models in Python and software engineers using Go.
• Work in a distributed cloud environment in GCP with a heavy dose of systems like Vertex AI, docker, and kubernetes.

We’re looking for someone who:

• Has 2+ years experience working with MLOps or Machine Learning Engineering in a production environment
• Has experience building infrastructure to support deploying machine learning models in production, including production architecture, monitoring and logging in a cloud compute environment
• Has experience programming with Python and the associated data science/machine learning packages (e.g., scikit-learn, pandas, xgboost, numpy, scipy)
• Has experience building large scale data warehouse solutions to support machine learning research

It would be amazing if:

• You have experience in Go and Solr
• You have experience with cloud compute and running services in Docker environments and Kubernetes
• You can communicate clearly and empathetically with developers, product managers and UX designers to explain the abilities and limitations of machine learning systems

You might want to know what our stack looks like - here’s a glimpse:

• GCP-based; heavy use of Cloud Datastore, BigQuery, Bigtable
• Kubernetes (GKE), terraform, helm
• Python, Jupyter and GCP based services for data science and machine learning
• General services written in Go; gRPC for APIs; prometheus for monitoring
• React and Typescript on the frontend
• Java, ObjC, and Rust for our mobile stack
• Monorepo for our code: we’re one big happy (in-sync!) family
• CircleCI-based continuous integration and team-based service ownership allow us to deploy software dozens of times per week

#LI-Remote #LI-JG1