HPE Labs - AI and Machine Learning Engineer

HPE Labs - AI and Machine Learning Engineer

This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge‑to‑cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next.

We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

We are seeking candidates interested in research and development of advanced technologies in Data‑centric and Trustworthy AI, including data and knowledge context retrieval, filtering, prioritization, generative AI model materialization, advanced reasoning and validation, to improve quality of AI agentic workflows. Successful candidates 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, 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.

• Familiarity with AI, Machine Learning and Deep Learning algorithms

• Experience with Generative AI:
  Large Language Models, 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)

• Experience with agentic AI platforms (e.g., Lang Graph, CrewAI, ADK, etc.)

• Strong programming skills in Python, C/C++, with high proficiency in data structures and algorithms

• Experience with CI/CD code development

• Outstanding analytical and problem solving skills

• Experience in deep learning research, GPU acceleration, and Model Optimization – a plus

• Expertise in research of data and workflow management systems – a plus

• Experience in system software performance and scalability optimization – a plus

• Experience with multi‑threaded programming, parallel processing, OOD/OOP/distributed programming – a plus

• Experience in containerized development and orchestration tools (e.g. Kubernetes, Ezmeral) – a plus

• Experience with hybrid AI‑HPC workflows (e.g., AI surrogate modeling, computational steering of experiments) – a plus

Additional

Skills:

Accountability, Action Planning, Active Learning, Active Listening, Agile Methodology, Agile Scrum Development, Analytical Thinking,…