Hardware Machine Learning PhD Research Internship

IMC•Chicago, IL

9h•$225,000

About The Position

We are deploying machine learning directly onto custom hardware – and we want you to help drive it forward. This PhD internship is an opportunity to work on research that has direct impact on IMC's work tackling open problems at the frontier of low-latency ML inference and hardware acceleration. You'll work alongside IMC engineers in one of the most demanding low-latency computing environments in the world. You'll own a focused research project from start to finish, present your findings to the team, and leave behind a prototype or benchmark that we can build on.

Requirements

Currently enrolled in a PhD program in Electrical Engineering, Computer Science, Physics, or a related field
Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
Understanding of machine learning fundamentals – neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
Proficiency in Python or similar languages for tooling, testing, and simulation
Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

Responsibilities

Architect and develop an ML focused research project based on a real-world trading environment
Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions
Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems
Present your project to the team, deepening our collective understanding of an area of ML acceleration
Gain hardware design fundamentals from skilled RTL developers and learn how they apply to our industry
Build skills to evaluate research not only from an academic perspective, but through real-world performance constraints, engineering costs, and industry impact