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# Machine Learning Engineer

- **Company:** Goliath Partners
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Big Data, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Pytorch, Embodied AI, Machine Learning Operations, Hardware Infrastructure, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_36d4fca1ba3e62a8e9df1290e9f06847

## About the Role

* Strong Python and software engineering fundamentals
 * Experience building ML infrastructure, training infrastructure, or large-scale distributed systems
 * Experience working with PyTorch and modern ML training stacks
 * Strong understanding of distributed computing and GPU-based workloads
 * Experience building reliable systems that support ML research or production ML
 * Comfortable working in a fast-moving environment with significant technical ownership, * Distributed training and large-scale model training
 * ML platforms / internal training infrastructure
 * GPU infrastructure and compute orchestration
 * Large-scale data infrastructure and pipelines
 * Performance optimization for ML workloads
 * Infrastructure supporting robotics, embodied AI, computer vision, or other real-world ML systems
 * Experience taking ML systems beyond research and into production or physical-world environments

This is a great opportunity for an engineer who wants to work on hard ML systems problems at scale while being much closer to the models and real-world applications than you would be on a traditional infrastructure team.

## Description

* Build and scale infrastructure for training large machine learning models
 * Develop distributed training systems and improve training efficiency, reliability, and throughput
 * Build data pipelines and infrastructure supporting large-scale ML workloads
 * Improve GPU utilization, compute orchestration, checkpointing, and experiment management
 * Develop tooling that enables researchers and ML engineers to iterate faster
 * Diagnose performance bottlenecks across training, data, and compute systems
 * Help take ML systems from experimentation through production and real-world deployment