AI/ML Engineer for HPC & AI Infrastructure (Hybrid)

Hewlett Packard Enterprise; Fort Collins (CO); Hybrid; USD 133,000 - 253,000; Full time

Job summary

Hewlett Packard Enterprise in Colorado is seeking an experienced software engineer to help build the next generation of AI-powered software for the world's largest supercomputers and AI infrastructure. You will help run observability, analytics, and AI-driven operations to keep systems resilient and scalable.

The role involves design and development across data engineering, model development, evaluation, deployment, monitoring, and continuous improvement, with a hybrid work model requiring about

Qualifications

• Strong software engineering fundamentals and production systems experience.
• Experience with Go, Python, React or equivalent.
• Experience building large-scale distributed systems and observability platforms.
• Experience applying AI/ML, GenAI, LLMs to real-world products.
• Strong problem-solving and ownership of complex technical challenges.
• Excellent communication and collaboration skills.

Responsibilities

• Design and build AI/ML capabilities for anomaly detection and automation.
• Develop production-quality software and services using Go, Python, React.
• Build AI solutions including ML models, LLM-powered workflows, analytics.
• Work across data engineering, model development, deployment, monitoring.
• Collaborate with engineers, product managers, and customers to solve scaling challenges.
• Drive innovation for AI-powered operations in large-scale environments.

Skills

Software engineering

Distributed systems

AI/ML

Problem solving

Communication

Tools

Go

Python

React

LLM workflows

Job description

Hewlett Packard Enterprise in Colorado is seeking an experienced software engineer to help build the next generation of AI-powered software for the world's largest supercomputers and AI infrastructure. You will help run observability, analytics, and AI-driven operations to keep systems resilient and scalable.

The role involves design and development across data engineering, model development, evaluation, deployment, monitoring, and continuous improvement, with a hybrid work model requiring about