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# AI Data Foundation Research Engineer

- **Company:** Hewlett-Packard Enterprise
- **Location:** Fort Collins, CO, United States
- **Experience:** Experienced
- **Salary:** $126,500.0 - $240,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Big Data, C++ (Programming Language), Computer Programming, Continuous Integration, Information Engineering, Data Infrastructure, Data Structures, Graph Database, Python (Programming Language), Machine Learning, Metadata, Natural Language Processing, Object-Oriented Software Development, Open Source Technology, Tensorflow, System Software, Workflow Management Systems, Multithreading, Pytorch, Large Language Models, Distributed Programming, Deep Learning, Data Representation, Parallel Computation, Generative AI, Scalability Testing, Data Layers, Kubernetes, Information Technology, Data Management, Machine Learning Operations, Virtual Agents
- **Published:** August 28, 2026
- **Apply:** https://hpe.wd5.myworkdayjobs.com/Jobsathpe/job/Ft-Collins-Colorado-United-States-of-America/AI-Data-Foundation-Research-Engineer_1201014-2

## About the Role

machine learning, deep learning, statistics, generative AI, data management, and big data pipelines, with good understanding of the current state of the art, major trends and opportunities, and a demonstrated track record in innovative research. The ideal candidate can thrive in an applied research environment, balancing significant technical contributions published externally in open source with the hands-on engineering skill to bring such contributions to practice in partnering with our internal software development teams and external partners., * PhD in Computer Science or related fields with a focus on data engineering and data science, in particular Machine Learning, Deep Learning, and/or data management for AI, plus 3 years of relevant industry experience.

 * Research experience in Generative AI, Deep Learning and Machine Learning
 * Experience with advanced AI model architectures: LLMs, Time Series Foundation Models, Diffusion Models, etc.
 * Expertise with end-to-end pipelines for AI and Machine Learning and in particular the data layer underlying the pipelines (e.g., DVC, Pachyderm, Common Metadata Framework)
 * Experience in AI model development lifecycle, ML/deep learning frameworks and MLOps platforms (e.g. Pytorch/Tensorflow, MLFlow, Kubeflow, Ray)
 * Experience with agentic AI platforms (e.g., LangGraph, CrewAI, ADK, LlamaIndex, etc.)

Preferred Skills

 * Strong programming skills in Python with high proficiency in data structures and algorithms. C/C++ skills
 * Experience with CI/CD code development
 * Outstanding analytical and problem-solving skills
 * Experience with hybrid AI-HPC workflows (e.g., AI surrogate modeling, computational steering of experiments)
 * Experience with knowledge graphs and knowledge infused learning
 * Expertise in research of data and workflow management systems
 * Experience in system software performance and scalability optimization
 * Experience with multi-threaded programming, parallel processing, OOD/OOP/distributed programming
 * Experience in containerized development and orchestration tools (e.g. Kubernetes, Ezmeral)

Additional Skills:

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

## Description

This role has been designed as 'Hybrid' with an expectation that you will work on average 2 days per week from an HPE office., Successful candidate will develop new methods for context discovery, retrieval, filtering, prioritization, multi-modal data representation, advanced reasoning, tool calling, and reasoning trace validation in conversational, deep research, and agentic AI workflows. Successful candidate will also work on development of capture, management, search, enhancement and interpretation of meta-data and lineage for AI pipelines that enable reproducibility, reuse and optimization of pipelines; discovery, selection and usage of relevant high quality data for trustworthy AI outcomes across multiple AI applications; development, evaluation and testing of Foundation AI models for different modalities: Natural Language Processing - NLP, Large Language Models - LLM, Time Series Analysis, Computer Vision, AI for Science, etc., and augmentation of AI models with structured knowledge (i.e., knowledge infused learning). We are particularly interested in individuals with a background in computer systems


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