Hardware Machine Learning PhD Research Internship

About the role

This Chicago-based PhD internship focuses on deploying machine learning onto custom hardware. You will own a research project addressing low-latency inference and hardware acceleration, collaborate with hardware engineers, evaluate emerging methods, present findings, and deliver a prototype or benchmark with practical engineering impact.

Responsibilities

• Architect and develop an ML-focused research project based on real-world use cases.
• Implement, verify, and deploy ML inference solutions alongside hardware engineers.
• Evaluate neural architecture search, ML systems, and quantization methods for measurable system improvements.
• Present project findings and deepen team understanding of machine learning acceleration.
• Develop hardware design fundamentals with skilled RTL developers.
• Assess research using performance constraints, engineering costs, and industry impact.

Requirements

• Currently enrolled in a PhD program in electrical engineering, computer science, physics, or a related field.
• Understand hardware constraints and trade-offs, including pipelining, resource utilization, and fixed-point arithmetic.
• Have experience with VHDL, SystemVerilog, HLS tools, or ML-to-hardware frameworks.
• Understand neural network architectures, inference optimization, quantization, and PyTorch or TensorFlow.
• Are proficient in Python or similar languages for tooling, testing, and simulation.
• Communicate effectively and collaborate across technical and non-technical disciplines.

Application Notes

• Applicants may submit one application per role each year.
• Applicants are encouraged to focus on the role best matching their skills and interests.
• Applicants not selected during the current season may reapply when the 2027 recruitment season begins.