We are seeking a talented and motivated FPGA Machine Learning Engineering Graduate Intern to join our dynamic team. Our mission is to develop innovative IP blocks and hardware solutions that enable customers to accelerate machine learning workloads on Altera FPGAs. As an intern, you will contribute to the development and evaluation of quantization schemes optimized for our hardware, empowering customers to achieve optimal performance and efficiency when deploying ML models on FPGAs.
You will gain hands-on experience with the FPGA AI Suite, including both hardware and software stacks, and work closely with senior engineers to analyze, design, and implement enhancements that improve the performance of ML workloads on Altera FPGAs. This is an exciting opportunity to innovate at the intersection of hardware and machine learning in a collaborative and creative
environment.
Our compensation is designed to reflect the Canadian labour market. The actual salary offered may vary based on several factors, including the position’s location, as well as the candidate’s experience, skills, training, and job-specific knowledge. In addition to base salary, we offer performance-based incentive opportunities that reward both individual contributions and overall company success.
Estimated Salary Range: $95K – $100K CAD
We use artificial intelligence to screen, assess, or select applicants for the position. This posting is for an existing vacancy. Canadian work experience is not required for this role.
Required Qualifications
Currently pursuing a Master’s or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
Solid understanding of digital hardware concepts (e.g., dataflow, pipelining, memory hierarchies).
Foundational knowledge of machine learning algorithms, especially quantization techniques and their hardware implications.
Experience with at least one of the following:
FPGA design (using Verilog, VHDL, or High-Level Synthesis tools)
Proficiency in C/C++, Python, or similar programming languages.
Strong analytical and problem-solving skills.
Ability to work effectively in a collaborative team environment.
Excellent verbal and written communication skills.
Experience with ML model deployment or inference on FPGA platforms.
Exposure to Altera FPGAs or similar architectures.
Knowledge of popular ML frameworks (e.g., TensorFlow, PyTorch) and their quantization workflows.