AI/ML Systems Engineering Intern (Summer 2027)

Internship Program Overview

Our Interns & Co-ops are our entry-level talent pipeline for GF across the globe. Our goal is to provide students with a meaningful work experience that will equip them with the skills to embark on a career in the fast-paced and growing semiconductor industry after graduation. As an intern at GF, you’ll experience one-on-one mentorship, work assignments that prioritize your growth and potential, professional development opportunities, and the chance to network with executives.

Summary of Role

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.

Essential Responsibilities include

• 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.

Other Responsibilities

• Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.

Required Qualifications

• Education – At least a sophomore at time of application and actively pursuing a Bachelor’s or Master’s in Electrical Engineering, Computer Engineering, or related field through an accredited degree program during the time of internship.
• Must have at least an overall 3.0 GPA and be in good academic standing.
• Coursework or project experience applying linear algebra, probability, numerical analysis, or related mathematical methods to engineering or computing problems.
• Working understanding of computer architecture concepts such as cache hierarchies, memory bandwidth, and CPU pipeline fundamentals.
• Experience using Python and data analysis tools such as NumPy, pandas, matplotlib, or similar.
• Language Fluency - English (Written & Verbal)
• Travel - Up to 10%
• Ability to work at least 40 hours per week during the internship.

Preferred Qualifications

• Prior related internship or co-op experience
• Demonstrated prior leadership experience in the workplace, school projects, competitions, etc.
• Strong interest in leveraging AI tools and emerging technologies to enhance productivity, streamline workflows, and support effective decision-making.
• Project management skills, i.e., the ability to innovate and execute on solutions that matter; the ability to navigate ambiguity.
• Strong written and verbal communication skills
• Strong planning & 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.
• #InternshipProgramUS
• Expected Salary Range $20.00 - $40.00
• Expected Salary Range $0.00 - $0.00

• The exact Salary will be determined based on qualifications, experience and location.
• An offer with GlobalFoundries is conditioned upon the successful completion of pre-employment conditions, as applicable, and subject to applicable laws and regulations.
• GlobalFoundries goal of excellence in business necessitates the attraction and retention of highly qualified people. Artificial barriers and stereotypic biases detract from this objective and may be illegally discriminatory.