HavocAI is an artificial intelligence company that offers maritime autonomy solutions to the Defense and Commercial markets. Backed by Drive Capital.
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.
What they're looking for
- 3+ years of experience in software engineering, infrastructure engineering, data engineering, ML infrastructure, or a related field
- Strong programming experience in Python, with experience in Go, C++, or another systems-oriented language preferred
- Experience building and operating production services, APIs, data pipelines, developer platforms, or infrastructure
- Hands-on experience with ML workflows such as dataset preparation, model training, evaluation, or deployment
- Experience with cloud infrastructure, preferably AWS, and Infrastructure as Code
- Hands-on experience with Kubernetes and containerized environments
More about this role
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 .
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...
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