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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. We are seeking candidates interested in research and development of advanced technologies in Data-centric and Trustworthy AI. 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.
Job Responsibility:
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
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)
Requirements:
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., LangGraph, 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
Nice to have:
Experience in deep learning research, GPU acceleration, and Model Optimization
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)
Experience with hybrid AI-HPC workflows (e.g., AI surrogate modeling, computational steering of experiments)