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# Machine Learning Infrastructure Engineer

- **Company:** True Talent Solutions LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Nvidia CUDA, Software Debugging, Distributed Computing Environment, Machine Learning, Workflow Management Systems, Data Logging, DeepSpeed, Machine Learning Operations, Software Version Control, Docker
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_89598935aae0a16a380a39e6cf6b8fe4

## About the Role

* 2 to 10 years of experience building large-scale machine learning infrastructure for pretrained or foundation models, including compute, data, inference, and distributed training systems.
 * Experience optimizing large-scale training, data, or inference workloads, including low-level GPU performance work with CUDA and JAX.
 * Proficiency with distributed training frameworks such as FSDP or DeepSpeed.
 * Experience with cloud platforms such as GCP, AWS, or Azure, and with containers and orchestration tools such as Docker and Kubernetes.
 * Familiarity with scalable model serving, distributed task management, monitoring, logging, observability, and version control practices.
 * Background in physical AI or AI for science, such as robotics, autonomous systems, or biology, and an interest in working across the machine learning lifecycle.
 * Comfort working in a fast-paced environment and learning unfamiliar domains.

## Description

Join an AI for science team building large-scale models for physical systems. You will develop the infrastructure behind distributed training and inference, helping the team advance models that can predict outcomes and inform effective actions., * Design, deploy, and maintain distributed machine learning training and inference clusters.

 * Build scalable end-to-end pipelines for petabyte-scale datasets and model training across the machine learning lifecycle.
 * Evaluate parallelization methods and numerical precision trade-offs at different model scales.
 * Profile and debug low-level GPU operations to improve performance.
 * Track relevant research and apply promising ideas to infrastructure and model workflows.