ML Cloud Infrastructure Engineer
ABOUT US:
Havoc is a leader in all-domain collaborative autonomy. Its software-defined hardware approach powers military and commercial-grade autonomous systems across sea, air, and land to sense, decide, and act together in complex and contested environments. Havoc connects assets, enabling them to share information, adapt in real time, and continue operating even when communications are disrupted or denied. Havoc optimizes mission performance and minimizes human risk.
Havoc was founded in 2024 and headquartered in Providence, Rhode Island. Learn more at Havoc: All-Domain Collaborative Autonomy.
ABOUT THE ROLE
As a Machine Learning Cloud Infrastructure Engineer, you will build and operate the infrastructure HavocAI teams use to train, evaluate, deploy, and monitor machine learning models safely and reliably.
You will develop the pipelines, services, platforms, and integrations connecting data lakes, telemetry stores, simulation environments, training workloads, cloud compute, and deployed models. Your work will enable autonomy, data, and software engineers to move efficiently from raw field and simulation data to reproducible datasets, scalable training, rigorous evaluation, and production deployment.
This role is ideal for a strong software, infrastructure, or data engineer with hands-on ML experience who enjoys building production systems at the intersection of data, models, compute, and autonomy. You should be comfortable operating in a fast-paced environment, solving ambiguous infrastructure problems, and building systems that must remain scalable, observable, secure, and reliable.
WHAT YOU’LL DO
ML INFRASTRUCTURE & PIPELINES
- Build pipelines that transform raw multi-modal data—including telemetry, imagery, video, sensor, and simulation data—into curated, versioned training datasets.
- Develop reproducible training and evaluation workflows that scale across cloud compute and GPU resources.
- Build and maintain model de...