Ph D/Research Intern - AI Research Lab

Overview

Research internship in the AI Research Lab focused on trustworthy agentic AI and generative AI foundation model systems, with particular emphasis on designing AgenticOS, related algorithms and evaluations.

What you'll do

• Define and prototype novel learning abstractions and mechanisms that make agentic AI reliable, shareable, scalable and evolvable, particularly for long-horizon applications.
• Work on data-centric AI techniques that capture, manage, search, enhance and interpret metadata and lineage for AI pipelines to enable reproducibility, reuse and optimization.
• Discover, select and use relevant high-quality data for trustworthy AI outcomes across multiple AI applications.
• Develop, evaluate and test foundation AI models across modalities such as NLP, LLMs, time series forecasting and anomaly detection, VLMs, diffusion models and AI for Science.
• Build multi-modal retrieval augmented generation workflows.
• Research advanced chain-of-thought, meta-reasoning, reinforcement learning and cognitive methods.
• Partner with internal software development teams and external partners to bring research contributions into practice.

What you'll need

• Second or later year PhD student in Computer Science or a related field with a focus on data engineering and data science, in particular machine learning, deep learning, generative AI and/or data management for AI.
• Background in computer systems, machine learning, deep learning, statistics, generative AI, data management or big data pipelines.
• Good understanding of the current state of the art and a demonstrated track record in innovative research.
• Ability to thrive in an applied research environment, balancing externally published, open-source technical contributions with hands-on engineering skill.

Nice to have

• Strong programming skills in Python, with high proficiency in data structures and algorithms.
• Outstanding analytical and problem-solving skills.
• Experience with generative AI: large language models, diffusion models, RAG, chain of thought, etc.
• Experience in the AI model development lifecycle, ML/deep learning frameworks and MLOps platforms (e.g. PyTorch/TensorFlow, MLflow, Kubeflow).
• Experience building or researching agentic AI systems, such as LLM-based agents, tool use, multi-agent orchestration, and agent frameworks and protocols (e.g. MCP, A2A).
• Experience in GPU and CPU 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 and 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).
• Familiarity with agent memory, state management and long-horizon execution (e.g. context management, checkpointing, recovery).
• Background in operating systems, scheduling, resource management, or LLM/agent serving and inference systems.
• Experience with trust, safety, security, observability or provenance for autonomous agents.
• Experience designing benchmarks, simulators or evaluation methodologies for agentic systems.
• Demonstrated ability to work across machine learning and systems, with publications or open-source contributions in either area.

Details

• Hybrid role: work on average 2 days per week from an HPE office.
• Preferred commitment of 6 months; 3-month internships considered for outstanding candidates who cannot commit to 6 months.