Staff Machine Learning Systems Engineer

🕒 March 18

🇺🇸 United States – Remote

💵 230k - 322k / year

⏰ Full Time

🔴 Lead

⚙️ Systems Engineer

👻 Ghost score 35%

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📋 Description

• 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

🎯 Requirements

• 8+ 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

🏖️ Benefits

• medical, dental, and vision insurance • 401(k) program with employer match • generous time off for vacation • parental leave

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