Onsite AI Data Foundation Research Engineer

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

Benefits offered by this job

Health & Wellbeing

Personal & Professional Development

Unconditional Inclusion

Job summary

Hewlett Packard Enterprise invites applications for an AI Data Foundation Research Engineer. The role involves building infrastructures to manage metadata and lineage for AI pipelines, enabling reproducibility and reuse, across NLP, vision, time-series and other modalities.

Successful candidates will advance Foundation AI models, augmenting them with knowledge context and enabling robust, observable agentic workflows while collaborating with internal teams and external partners.

Qualifications

• PhD in Computer Science or related field with focus on data engineering/data science plus 3 years of relevant industry experience.
• Strong research background in Generative AI, DL/ML; excellent theoretical and practical skills.

Responsibilities

• Develop infrastructures and algorithms to capture/metadeta for AI pipelines to enable reproducibility.
• Work on data discovery, selection and usage for trustworthy AI across multiple AI applications.
• Prototype and evaluate Foundation AI models for NLP, LLM, Time Series, CV, etc.
• Augment AI models with structured knowledge and knowledge context retrieval.

Skills

Generative AI

Deep Learning

Machine Learning

Python

PyTorch

TensorFlow

MLOps

Kubernetes

LangGraph

Data Structures

Algorithms

Education

PhD in Computer Science or related field with focus on data engineering/data science

Tools

DVC

lakeFS

Pachyderm

Common Metadata Framework

Flowcept

Kubeflow

Ezmeral

Job description

Hewlett Packard Enterprise invites applications for an AI Data Foundation Research Engineer. The role involves building infrastructures to manage metadata and lineage for AI pipelines, enabling reproducibility and reuse, across NLP, vision, time-series and other modalities.

Successful candidates will advance Foundation AI models, augmenting them with knowledge context and enabling robust, observable agentic workflows while collaborating with internal teams and external partners.