Company:Qualcomm Canada ULC Job Area:Engineering Group, Engineering Group Machine Learning Engineering General Summary: As a member of the Low Power AI Solution team, you will conduct advanced research on model efficiency, model compression techniques, and ML
Locations: MARKHAM, Canada Categories: Engineering Req ID: 90266 Hiring Target Min: CAD $132,000.00/Yr. Hiring Target Max: CAD $198,000.00/Yr. ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology can change lives for the better. It
Company: Qualcomm Canada ULC Job Area: Engineering Group, Engineering Group Machine Learning Engineering General Summary: As a member of the Low Power AI Solution team, you will conduct advanced research on model efficiency, model compression techniques,
Company: Qualcomm Canada ULC Job Area: Engineering Group, Engineering Group Machine Learning Engineering General Summary: As a member of the Low Power AI Solution team, you will conduct advanced research on model efficiency, model compression techniques,
# Data ScientistApply: Hybrid: Canadian Head Office: Full time: Posted Today: R34152At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years,
Department: R&D Location: Markham, ON, Canada Responsibilities Architect solutions for digital signal processing applications, implementing DSP algorithms, design and maintain codes running on FPGA and other heterogeneous platforms, test, debug and refine solutions Requirements Background in digital signal
Department: R&D | Location: Markham, ON, Canada We are looking for DSP engineers responsible for working on DSP algorithm design and implementation on heterogeneous platforms, including CPU, GPU, and FPGA. Responsibilities Architect solutions for digital signal processing
Company: Qualcomm Canada ULC Job Area: Engineering Group, Engineering Group Machine Learning Engineering General Summary: As a member of the Low Power AI Solution team, you will conduct advanced research on model efficiency, model compression techniques,