Aml Intern Jobs in California (NOW HIRING)

About The Role

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.

What You Might Do

• 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.

Essential Qualifications 

• 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.

Preferred Skills

• 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.

Hourly Pay Range: $45.00-$51.00 per hour. Actual compensation will be determined based on factors such as skills, qualifications, and experience.

2027 Internship Program Dates: May/June-August/September 2027. Final dates will be based on student schedules.