Hardware Machine Learning PhD Research Internship

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

Benefits - US | IMC Trading

IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone.

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