Senior Machine Learning Systems Engineer

What you'll do

• Design end-to-end MLOps patterns including data preparation, model management, and experiment tracking.
• Develop and support a graph ML codebase that abstracts common patterns for better scalability.
• Perform performance tuning to improve training time, efficiency, and GPU costs in distributed environments.
• Optimize batch data processing using tools like Apache Beam, Spark, and Ray Data.
• Architect pipelines to build and maintain massive graph data structures with billions of nodes and edges.
• Manage and integrate MLOps tools for experiment tracking, model serving, and model registries.

What we're looking for

• 5+ years of experience in ML infrastructure, including model training and deployment.
• Hands-on experience with ML optimization, specifically memory and GPU profiling.
• Deep experience with cloud technologies like GCP BigQuery, Google Cloud Storage, and Terraform.
• Experience administering MLOps tools for experiment tracking, model serving, and registries like MLflow or Wandb.
• Proficiency in common ML programming languages and frameworks such as Python, PyTorch, and TensorFlow.
• Deep experience with distributed training frameworks including Ray and Kubernetes.
• Experience with graph databases (Neo4j, JanusGraph, TigerGraph) is a plus.
• Experience with graph neural networks (GNNs) and frameworks like PyTorch Geometric or DGL is a plus.