Hardware Machine Learning PhD Research Internship
From $225k • Chicago, United States • Internship
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Internship
Categories
Machine Learning Engineer
About the job
We are deploying machine learning directly onto custom hardware, and this PhD internship offers the chance to contribute to research that directly impacts our work on low-latency ML inference and hardware acceleration. You will collaborate with IMC engineers in a demanding low-latency computing environment, taking ownership of a focused research project from inception to completion. Your work will culminate in presenting findings to the team and delivering a prototype or benchmark for future development.
Responsibilities
- Architect and develop an ML-focused research project based on real-world use cases.
- Implement, verify, and deploy ML inference solutions in collaboration with hardware engineers.
- Track and evaluate emerging research in neural architecture search, ML systems, and quantization methods for system improvements.
- Present project findings to the team to enhance collective understanding of ML acceleration.
- Gain hardware design fundamentals from RTL developers and learn their application in the industry.
- Develop skills to evaluate research based on real-world performance, engineering costs, and industry impact.
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) for mapping ML models onto FPGAs or custom ASICs.
- Experience with hardware fundamentals (VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI).
- Understanding of machine learning fundamentals, including neural network architectures, inference optimization, quantization techniques, and ML frameworks (PyTorch/TensorFlow).
- Proficiency in Python or similar languages for tooling, testing, and simulation.
- Strong communication and collaboration skills across technical and non-technical teams.
Benefits
- Discretionary bonus
- Paid leave
- Insurance