What You'll Accomplish
- Unlock offline & real-time access to trillions of data points for our ML and Data Science teams
- Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing
- Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray
- Partner with other teams and business stakeholders to deliver ML and AI initiatives
Your Expertise
- You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store
- You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools
- You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance
- You have led the rollout and operationalization of feature stores such as Tecton or Feast
- You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs
What We Use
- Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
- Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
- Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
- Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
- Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas
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