Hardware Machine Learning PhD Research Internship

Your Core Responsibilities

• Architect and develop an ML focused research project based on real-world use cases
• 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

Your Skills and Experience

• 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