FPGA Machine Learning Engineering - Graduate Intern

Responsibilities

• Develop innovative IP blocks and hardware solutions to accelerate machine learning workloads on Altera FPGAs
• Contribute to the development and evaluation of quantization schemes optimized for hardware
• Gain hands-on experience with the FPGA AI Suite, including both hardware and software stacks
• Work closely with senior engineers to analyze, design, and implement enhancements for ML workload performance

Requirements

• Currently pursuing a Master’s or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field
• Solid understanding of digital hardware concepts such as dataflow, pipelining, and memory hierarchies
• Foundational knowledge of machine learning algorithms, specifically quantization techniques and hardware implications
• Experience with FPGA design (Verilog, VHDL, or HLS) or proficiency in C/C++ and Python
• Strong analytical, problem-solving, and collaborative communication skills

Preferred Qualifications

• Experience with ML model deployment or inference on FPGA platforms
• Exposure to Altera FPGAs or similar architectures
• Knowledge of popular ML frameworks like TensorFlow or PyTorch and their quantization workflows

About the Company

Altera provides leadership programmable solutions that are easy-to-use and deploy in applications from cloud to edge, offering limitless AI possibilities. Our end-to-end broad portfolio of products includes FPGAs, CPLDs, Intellectual Property, development tools, System on Modules, SmartNICs, and IPUs.