Hardware Machine Learning PhD Research Internship
Core
Architect and develop ML-focused research projects to deploy machine learning directly onto custom hardware for low-latency inference.
Role type
PhD Research Intern (Hardware Machine Learning)
Builds
Prototype ML inference solutions and benchmarks for custom ASICs/FPGAs
Domain
Hardware acceleration, low-latency ML inference, financial trading infrastructure
Deliverable
production ML models
Required skills
Neural network architectures, inference optimization, quantization techniques, VHDL/SystemVerilog, HLS tools, ML-to-hardware frameworks (hls4ml, FINN, Vitis AI), Python
Preferred skills
Fixed-point arithmetic, pipelining, resource utilization, real-world performance constraints evaluation
Technologies
PyTorch, TensorFlow, hls4ml, FINN, Vitis AI, SystemVerilog, VHDL
Responsibilities
Implement, verify, and deploy ML inference solutions; track and evaluate emerging research in neural architecture search and quantization; present research findings to the team
Seniority
PhD Candidate (Research Intern)
