Software Engineering Intern, AIML Platform - HP IQ

HP•San Francisco, CA

4d•$40 - $50

About The Position

HP IQ's AI Machine Learning (AML) team is building a foundational platform for agentic devices. The platform supports the lifecycle of AI models, from creation and fine-tuning to optimized inference and orchestration, helping complex AI capabilities run efficiently on-device. The internship contributes to one or more areas: model inference optimization, orchestration and agent workflows, or model creation and fine-tuning. Interns work with experienced engineers and contribute features for HP's next-generation devices, gaining exposure to how parts of an end-to-end AI system integrate and scale. The role may involve profiling, benchmarking, and accelerating model execution on resource-constrained devices; experimenting with quantization, distillation, and other optimization techniques; building or extending orchestration frameworks and agent workflows; adapting pre-trained language models for specific use cases; and evaluating experiments. Interns collaborate with system software, design, and product teams, communicate technical progress and decisions, and may contribute to open-source projects or internal libraries. This is part of HP's 2027 Internship Program, scheduled for May/June through August/September 2027, with final dates based on student schedules. The stated hourly pay range is $40.00–$50.00. The posting describes a competitive benefits package for full-time employees, including health, dental, and vision insurance; disability and life insurance; an employee assistance program; a flexible spending account; and paid leave policies.

Requirements

Currently pursuing a degree in Computer Science, Engineering, or a related field, or have equivalent practical experience.
Some coursework or project experience in machine learning, systems engineering, or software development.
Ability to clearly explain project goals, outcomes, challenges, and technical decisions to peers and mentors.
Familiarity with at least one of model inference and optimization, orchestration frameworks, model fine-tuning and adaptation, or similar systems-level AI work.
Collaboration and ownership mindset, including comfort working cross-functionally, asking for help, and taking responsibility for outcomes.
Ability to operate with some ambiguity and proactively seek clarity.
Ability to debug issues, evaluate technical tradeoffs, and make reasonable decisions among multiple approaches.

Nice To Haves

Open-source contributions to inference engines such as vLLM, model repositories, or orchestration libraries.
Experience with Python and ML-adjacent libraries such as PyTorch, TensorFlow, Hugging Face, or FastAPI.
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, including profiling, benchmarking, and accelerating model execution on resource-constrained devices.
Experiment with quantization, distillation, or other techniques to reduce model latency and memory footprint.
Contribute to orchestration frameworks and agent workflows that coordinate models and agents, potentially using LangGraph or LangChain.
Participate in model customization and fine-tuning by adapting pre-trained language models, evaluating results, and iterating on configurations.
Collaborate with system software, design, and product teams to integrate work into the broader platform.
Communicate progress, challenges, and technical decisions, and seek mentorship from experienced engineers.
Contribute to relevant open-source projects or internal libraries.
Take ownership of end-to-end outcomes and help improve platform capabilities and reliability.