Improve engineering productivity and creativity through the application of large language models (LLMs) and AI, making engineering processes faster, easier, and more effective.
Requirements
- Strong programming skills in Python.
- Experience machine learning fundamentals, especially in natural language processing or LLMs.
- Exposure to hardware design or verification workflows (RTL, simulation, formal verification).
- Familiarity with retrieval-augmented generation (RAG), vector search, or multi-agent systems.
- Contributions to open-source AI/ML projects or research publications.
- Awareness of responsible AI practices and evaluation methods.
Responsibilities
- Build AI tools that help engineers solve problems, from interactive agents to workflow automation.
- Turn prototypes into scalable AI applications that work across large teams and environments, by fine-tuning prompts, pipelines, and infrastructure to improve performance, speed, and cost efficiency.
- Collaborate with senior engineers to write clean, well-tested software that’s easy to maintain and improve, while developing your skills through mentorship and feedback.
- Work closely with experts in hardware design, software development, and verification to understand real needs.
- Explore new AI tools and techniques, bringing in fresh ideas for our engineering challenges.
- Share your work and help shape our growing knowledge base for AI at Arm.
Other
- Are currently enrolled and studying towards a Machine Learning, Computer Science, or Computer Engineering Degree (Bachelors or Masters students welcome).
- Candidates with alternative degrees will also be considered if they have relevant experience.
- Arm Internships require you to be enrolled in a higher education degree and be returning to your course after your internship/placement.
- If you are graduating before September 2026, you will not be eligible for an Intern role but you will be eligible for our graduate roles.
- This is a non-exempt hourly role which will be paid at an hourly rate based on the number of hours worked.
