AI/ML Systems Engineer (2026 New College Graduate)

Job Title

AI/ML Systems Engineer (2026 New College Graduate)

Role Summary

Early-career engineer responsible for workload characterization, performance modeling, and hardware mapping for AI/ML inference and training workloads. The role works with SoC, IP, compiler, and runtime teams to estimate performance KPIs and guide architecture and software tradeoffs.

This position sits at the intersection of machine learning, computer architecture, and systems optimization and requires rigorous quantitative analysis and clear technical communication.

Experience Level

Entry-level. Targeted at new college graduates; appropriate for candidates with approximately 0–2 years of relevant industry or internship experience.

Responsibilities

The core responsibilities focus on analyzing AI/ML workloads, building predictive performance models, and communicating findings to architecture and software teams.

• Profile and characterize AI/ML workloads (CNNs, transformers, RNNs and emerging models) across inference and training stacks.
• Identify compute, memory bandwidth, and power bottlenecks using roofline analysis, operational intensity profiling, and bottleneck decomposition.
• Map workload demands to hardware capabilities and support design tradeoff discussions (ISA, memory subsystem sizing, on-chip vs off-chip bandwidth).
• Build and maintain spreadsheet- or code-based models projecting throughput, latency, and efficiency for candidate architectures; validate models against silicon or simulation data.
• Engage with compiler and runtime teams to assess kernel optimization, scheduling, and memory layout effects on performance.
• Document and present analysis results in written reports, presentations, and design reviews.
• Follow Environmental, Health, Safety & Security practices in all activities.

Requirements

Key technical and professional requirements. Must-haves are listed first; preferred skills follow.

• Must-have: Strong quantitative and mathematical reasoning; ability to derive and manipulate analytical performance models from first principles.
• Must-have: Practical experience or coursework using Python for analysis and reproducible modeling.
• Must-have: Familiarity with AI/ML model types and the ability to reason about operator-level arithmetic intensity, bandwidth utilization, and latency under queuing or pipeline constraints.
• Must-have: Clear technical writing and verbal communication; able to present findings to hardware and software specialists.
• Nice-to-have: Exposure to ML compiler toolchains (MLIR, IREE, TVM) or experience with compiler/runtime pipelines.
• Nice-to-have: Prior internship or co-op experience in systems engineering, hardware architecture, or performance engineering.
• Nice-to-have: Knowledge of RISC-V architecture and vector/matrix extensions; prior work on edge or mobile SoC workload characterization.

Education Requirements

Graduating with a Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field from an accredited program. Acceptable for candidates with 0–2 years of relevant industry or internship experience. Minimum overall GPA of 3.0 required. English fluency (written and verbal) required.

About the Company

Company: GlobalFoundries

Headquarters: Saratoga Springs, New York, USA

GlobalFoundries is a leading contract manufacturer for the global semiconductor industry, with facilities in multiple countries, including the USA. The company develops a broad portfolio of semiconductor technologies and employs around 13,000 people worldwide. GlobalFoundries focuses on enhancing competitiveness in specialized application solutions and fostering innovation in mobile communications, consumer electronics, and automotive applications.