Research Software Engineer Internships

Research Intern — Neural Network Training and Hardware–Software Co-Design Responsibilities:

• Design, train, and evaluate neural networks for on-device workloads under compute, memory, and energy constraints
• Develop hardware-aware training and model compression techniques and measure their impact on target hardware
• Explore partitioning models across multiple accelerators or chips, including how inter-chip communication affects accuracy and performance
• Profile models end to end and use the measurements to drive both model and hardware design decisions
• Prototype and validate on FPGA platforms, modifying and debugging existing RTL as needed
• Present results through a working demo and a technical write-up

Minimum Qualifications:

• Hands-on experience training deep neural networks in PyTorch or TensorFlow, from data pipeline through debugging and evaluation
• Working knowledge of computer architecture: how memory hierarchy, data movement, and numerical precision affect neural network performance
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
• Intent to return to full-time degree program after completion of the internship

Preferred Qualifications:

• RTL design in Verilog or SystemVerilog, including simulation and testbenches
• FPGA experience: synthesis, timing closure, and on-board bring-up and debug
• Quantization-aware training, mixed precision, pruning, or knowledge distillation
• Experience with neural network accelerators, model parallelism, or distributed inference
• C/C++ and model deployment on embedded or edge platforms
• Publications or substantial projects in efficient ML, ML systems, or computer architecture (e.g., MLSys, NeurIPS, ICLR, ISCA, MICRO, DAC)

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