Leads development of large-scale ML platforms, focusing on MLOps, graph ML infrastructure, performance tuning, and distributed training optimization. Requires 5+ years in ML infrastructure with expertise in PyTorch, Kubernetes, Ray, and cloud tools.
217k – 303k
Remote5+ YOEML Engineering
About the role
What You’ll Do
Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more
Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration
Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment
Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more
Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges
Who You Might Be
5+ years of experience in ML infrastructure, including model training and model deployments
Hands-on experience with ML optimization, including memory and GPU profiling
Deep experience with cloud-based technologies for supporting an ML platform, including tools like GCP BigQuery, Google Cloud Storage, infrastructure-as-code (Terraform), and more
Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries (e.g. MLflow or Wandb)
Proficiency with the common programming languages and frameworks of ML, such as Python, PyTorch, Tensorflow, etc.
Deep experience working with distributed training frameworks, including Ray and Kubernetes
Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle.
Strong organizational & communication skills
Experience working with graph databases (Neo4j, JanusGraph, TigerGraph) is a big plus
Experience working with graph neural networks (GNNs) and associated graph ML frameworks (PyTorch Geometric, Deep Graph Library) is a big plus
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