Software Engineering Intern, AML Platform (Summer 2027)

HP IQ•San Francisco, CA

2d•$45 - $51•Remote

About The Position

HP IQ's AI Machine Learning (AML) team is building the foundational platform powering a new generation of agentic devices. This platform orchestrates the complete lifecycle of AI models: from creation and fine-tuning through optimized inference and intelligent orchestration. The team works to make complex AI capabilities run efficiently on-device, enabling locally-deployed agentic experiences that reduce token costs and improve privacy. In this internship role, you will contribute to one or more core pillars of the AML platform: model inference optimization, orchestration and agent workflows, or model creation and fine-tuning. You will partner closely with experienced engineers to ship features that directly impact HP's next-generation devices and gain visibility into how each component of an end-to-end AI system integrates and scales.

Requirements

Currently pursuing a degree in Computer Science, Engineering, or related field, or equivalent practical experience.
Some coursework or project experience in machine learning, systems engineering, or software development is expected.
Demonstrated technical communication skills: ability to articulate project goals, outcomes, challenges, and technical decisions clearly; comfort explaining technical concepts to peers and mentors.
Genuine familiarity with one or more of the following: model inference and optimization, orchestration frameworks (LangGraph, LangChain), model fine-tuning and adaptation, or similar systems-level AI work. Project experience, open-source contributions, or course projects demonstrating depth in at least one area are highly valued.
Collaboration and ownership mindset: comfort working in cross-functional teams, asking for help when needed, and taking responsibility for outcomes; ability to operate with some ambiguity and seek clarity proactively.
Problems-solving orientation: ability to debug issues, evaluate technical tradeoffs, and make reasonable decisions when multiple approaches are viable.

Nice To Haves

Open-source contributions to inference engines (e.g., vLLM), model repositories, or orchestration libraries; demonstrated ability to contribute meaningfully to a shared codebase.
Experience with Python and ML-adjacent libraries (PyTorch, TensorFlow, HuggingFace, FastAPI, or similar).
Familiarity with on-device or edge AI constraints and optimization techniques.
Experience with system-level profiling and performance optimization.

Responsibilities

Work on model hosting and inference optimization, learning how to profile, benchmark, and accelerate model execution on resource-constrained devices; experiment with quantization, distillation, or other optimization techniques to reduce latency and memory footprint.
Contribute to orchestration frameworks and agent workflows, building or extending systems that coordinate multiple models and agents using tools like LangGraph or LangChain; develop an understanding of when agentic patterns are appropriate and how to design robust, scalable orchestrations.
Participate in model customization and fine-tuning, taking pre-trained language models and adapting them for specific use cases; conduct experiments, evaluate results, and iterate on model configurations to improve performance for target tasks.
Collaborate cross-functionally with system software, design, and product teams to understand how your work integrates into the broader platform; communicate progress, challenges, and technical decisions clearly and seek mentorship from engineers with deep expertise in your focus area.
Contribute to open-source projects or internal libraries relevant to inference, orchestration, or model training; demonstrate ownership of end-to-end outcomes and ownership of improving the platform's capabilities and reliability.