Staff Machine Learning Systems Engineer

About the role

Our partner is looking for a Staff Machine Learning Systems Engineer based in the United States. This senior technical role focuses on building and scaling the infrastructure that powers large-scale machine learning systems. You will design end-to-end MLOps patterns that accelerate model development, experimentation, deployment, and iteration. The position combines platform engineering, distributed ML, data processing, performance optimization, and cloud infrastructure. You will help develop scalable graph machine learning platforms capable of handling billions of nodes and tens of billions of edges. Working closely with machine learning engineers, you will improve training performance, resource efficiency, reliability, and GPU utilization. The role offers significant technical ownership in a distributed, cloud-based environment where scalability and ease of use are critical. You will also serve as a strong advocate for platform users while shaping engineering practices and infrastructure that enable teams to move faster.

Responsibilities

• Design and implement end-to-end model lifecycle patterns and MLOps capabilities covering data preparation, model management, experiment tracking, deployment, and related workflows.
• Lead zero-to-one development of graph machine learning infrastructure and codebases that abstract common patterns and enable scalable model development.
• Collaborate with machine learning engineers to optimize model performance, training duration, memory utilization, and GPU costs across large distributed environments.
• Optimize batch data processing and data pipelines using technologies such as Apache Beam, Apache Spark, Ray Data, and cloud data warehouse services.
• Architect pipelines capable of building and maintaining massive graph data structures containing billions of nodes and tens of billions of edges.
• Build and evolve cloud-based infrastructure supporting machine learning platforms, with an emphasis on scalability, reliability, performance, and operational efficiency.
• Administer and integrate MLOps tools for experiment tracking, model serving, and model registries.
• Develop solutions that improve the overall machine learning development lifecycle and make platform capabilities easier and more efficient for engineering teams to use.
• Partner with cross-functional technical teams to understand infrastructure needs, identify bottlenecks, and deliver scalable solutions.
• Establish and promote engineering patterns that improve platform reliability, developer productivity, model iteration, and ease of use.
• Provide technical leadership across complex infrastructure initiatives and help shape the long-term architecture of machine learning systems.