AI and Machine Learning Engineer
$120,500–$276,500 year
HybridSpring, Texas, United States
Job Summary
Installs and configures complex IT infrastructure components, including servers, storage, and network systems, while developing software scripts to automate deployment. Performs system-level analysis of server workloads running deep learning and machine learning code on HPE platforms, utilizing InfiniBand and high-speed networks. Studies and improves Large Language Model performance on HPE GPU servers, runs AI and HPC benchmarks, and analyzes logs and traces to understand workload behavior. Writes white papers guiding AI workload and model selection, and communicates technical summaries to non-technical colleagues. Works with software and hardware partners to optimize systems and resolve performance issues, providing guidance to less-experienced staff.
Required Qualifications
- Master's degree or PhD in Computer Science, Engineering, Information Technology or Systems, or relevant field
- 5+ years of experience
- 5+ years of experience in Machine Learning/Artificial Intelligence
- 5+ years of experience in HPC
- Experience running NCCL, HPL and AI benchmarks
- Experience working with containers and distributed deep learning and neural networks, to include transformers used in generative AI projects
- Experience working with High Performance Computer Servers, High Performance Networking, and associated software, including resource managers like Slurm
- Experience working with Weka I/O, NFTS and Lustre File Systems
- Programming experience in Python, C, C++
- Strong analytical and critical thinking skills
- Must be a self-starter and be able to work with minimum supervision in a semi-remote setting
- Work on average 2 days per week from an HPE office
Desired Qualifications
- Scripting, process automation and CI/CD are strongly desired
- Artificial Intelligence Technologies
- Cross Domain Knowledge
- Data Engineering
- Data Science
- Design Thinking
- Development Fundamentals
- Full Stack Development
- IT Performance
- Machine Learning Operations
- Scalability Testing
- Security-First Mindset
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