AI/ML Systems Engineer (2027 New College Graduate)
About The Position
GlobalFoundries is seeking an early-career AI/ML Systems Engineer to advance workload characterization and performance modeling across machine learning systems. The engineer will own quantitative analyses that connect workload behavior to hardware and software design decisions and communicate results across architecture, compiler, runtime, and product teams. This role is part of GlobalFoundries' full-time new college graduate program, which offers accelerated training in a fast-paced work environment, cross-functional opportunities, talent mobility, mentorship, networking, and leadership opportunities.
Requirements
- Graduating with a bachelor's or master's degree in Electrical Engineering, Computer Engineering, or a related field from an accredited degree program.
- Overall GPA of at least 3.0 and proven good academic standing.
- Strong mathematical reasoning, including ability to apply linear algebra, probability, and numerical analysis to performance estimation and numerical precision questions.
- Working understanding of computer architecture concepts, including cache hierarchies, memory bandwidth, and CPU pipeline fundamentals.
- Experience with Python and data analysis tooling such as NumPy, pandas, matplotlib, or similar.
- Exposure to machine learning frameworks such as PyTorch or TensorFlow.
- English fluency, written and verbal.
Nice To Haves
- Prior related internship or co-op experience.
- Demonstrated 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 meaningful solutions and navigate ambiguity.
- Strong written and verbal communication skills.
- Strong planning and organizational skills.
- Familiarity with the roofline model, FLOP counting, operator-level profiling, quantization analysis, or performance KPI frameworks.
- Exposure to compilation or intermediate representation concepts, including MLIR, IREE, TVM, or similar AI compiler infrastructure.
Responsibilities
- Characterize AI/ML inference and training workloads, quantify compute and memory behavior, and identify bottlenecks using methods such as roofline analysis, operational intensity profiling, and bottleneck decomposition.
- Build and validate analytical or code-based models to estimate achievable throughput, latency, utilization, bandwidth consumption, and efficiency for real or projected hardware.
- Partner with hardware, compiler, and runtime engineers to translate workload findings into architectural and software optimization discussions, including memory subsystem sizing, scheduling, kernel optimization, and data layout tradeoffs.
- Own benchmarking or modeling efforts end to end, maintain clear and reproducible analysis artifacts, and communicate conclusions through written reports, presentations, and design reviews.
- Perform all activities safely and responsibly and support Environmental, Health, Safety & Security requirements and programs.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Entry Level
