AI/ML Systems Engineering Intern (Summer 2027)

GlobalFoundries•Richardson, TX

5d•$20 - $40

About The Position

GlobalFoundries is seeking a curious and motivated AI/ML Systems Engineering Intern to characterize machine learning workloads and analyze how their compute and memory demands map to hardware resources. Working with experienced hardware and software engineers, the intern will build practical skills in performance experimentation, analytical modeling, and clear technical communication. The role is part of GlobalFoundries’ summer internship program, which offers mentorship, growth-oriented assignments, professional development, and opportunities to network with executives.

Requirements

Be at least a sophomore at the time of application and actively pursuing a Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, or a related field through an accredited degree program during the internship.
Have at least a 3.0 overall GPA and be in good academic standing.
Have coursework or project experience applying linear algebra, probability, numerical analysis, or related mathematical methods to engineering or computing problems.
Have a working understanding of computer architecture concepts such as cache hierarchies, memory bandwidth, and CPU pipeline fundamentals.
Have experience using Python and data analysis tools such as NumPy, pandas, matplotlib, or similar.
Have written and verbal fluency in English.
Be able to work at least 40 hours per week during the internship.
Be able to travel up to 10%.

Nice To Haves

Prior related internship or co-op experience.
Demonstrated prior leadership experience in the workplace, school projects, competitions, or similar settings.
Strong interest in leveraging AI tools and emerging technologies to enhance productivity, streamline workflows, and support effective decision-making.
Project management skills, including the ability to innovate and execute on solutions that matter and navigate ambiguity.
Strong written and verbal communication skills.
Strong planning and organizational skills.
Exposure to PyTorch or TensorFlow and concepts such as FLOP counting, operator-level profiling, or the roofline model.
Exposure to compilation, intermediate representations, or AI compiler toolchains such as MLIR, IREE, TVM, or similar.

Responsibilities

Run and interpret performance experiments for AI/ML workloads, including classical inference pipelines and modern model architectures, and document observations using reproducible analysis methods.
Build analytical models to estimate throughput, latency, utilization, operational intensity, and memory bandwidth requirements, and identify likely compute or memory bottlenecks.
Collaborate with hardware and software engineers to connect workload behavior with architectural and implementation decisions, including cache, memory subsystem, runtime, and compiler considerations.
Present findings through concise reports, visualizations, and technical discussions, and grow toward independent ownership of a defined workload characterization study during the internship.
Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.