ML Cloud Platform Engineer — Scalable Pipelines

HavocAI; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Life Insurance

401k

Unlimited PTO

Equity

Home office stipend

Global Entry

Parental leave

Wellness stipend

Job summary

HavocAI in Providence, Rhode Island, is seeking a Machine Learning Cloud Infrastructure Engineer to build and operate the infrastructure used to train, evaluate, deploy, and monitor ML models safely and reliably.

You will design pipelines transforming multi-modal data into curated datasets, scale training across cloud and GPU resources, and implement deployment infrastructure with reproducibility, observability, and security at the core.

Qualifications

• 3+ years of software, infrastructure, data, or ML infra experience.
• Strong Python programming with Go, C++ or similar systems language preferred.
• Experience building and operating production services, APIs, data pipelines, or infrastructure.
• Hands‑on ML workflows including dataset prep, training, evaluation, or deployment.
• Cloud infrastructure expertise, ideally AWS, with IaC and Kubernetes experience.
• Strong reliability, observability, testing, automation, and maintainability.

Responsibilities

• Build ML infrastructure pipelines for data, training, evaluation, and deployment.
• Develop scalable pipelines transforming multi‑modal data into training datasets.
• Design and operate AWS/Kubernetes infrastructure and CI/CD for ML workloads.
• Implement monitoring, logging, tracing, and observability across pipelines and models.
• Collaborate with Autonomy, Software, Data, Simulation, and Security teams.

Skills

Python

Go

C++

Production services

ML workflows

Data pipelines

Cloud infrastructure

Kubernetes

AWS

CI/CD

Tools

AWS

Kubernetes

Terraform

CI/CD tooling

Job description

HavocAI in Providence, Rhode Island, is seeking a Machine Learning Cloud Infrastructure Engineer to build and operate the infrastructure used to train, evaluate, deploy, and monitor ML models safely and reliably.

You will design pipelines transforming multi-modal data into curated datasets, scale training across cloud and GPU resources, and implement deployment infrastructure with reproducibility, observability, and security at the core.