Machine Learning Framework, Compiler & Performance Engineer

Qualcomm•Markham, ON

7d•CA$99,500 - CA$149,300

About The Position

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state-of-the-art machine learning solutions over a broad set of technology verticals or designs. Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. This is a replacement position.

Requirements

Demonstrated ability to learn, think and adapt in fast changing environment
Detail-oriented with strong problem-solving, analytical and debugging skills
Strong communication skills (written and verbal)
Strong background in algorithm development and performance analysis is essential
Strong object-oriented design principles
Strong knowledge of C++
Strong knowledge of Python
Experience in compiler design and development is an asset
Knowledge of network model formats/platforms (eg. Pytorch, ONNX) is a strong asset.
Knowledge of software development processes (revision control, CD/CI, etc.)
Familiarity with tools such as git, Jenkins, Docker, clang/MSVC
On-silicon debug skills of high-performance compute algorithms
Knowledge of algorithms and data structures
Knowledge of computer architecture, digital circuits and event-driven transactional models/simulators
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.

Responsibilities

Contributing to the development and evolution of ML/AI compilers within Qualcomm
Defining and implementing algorithms for compiling ML/AI workloads to achieve high performance and low power on Qualcomm HW
Creating and implementing algorithms that couple PyTorch framework efficiently to Qualcomm ML/AI Compiler flows.
Understanding trends in ML network design, through customer engagements and latest academic research, and how this affects both SW and HW design
Exploration and analysis of performance/area/power trade-offs for future HW and SW ML algorithms
Creation of performance-driven simulation components (using C++, Python) for analysis and design of high-performance HW/SW algorithms on future SoCs
Pre-Silicon prediction of performance for various ML algorithms
Running, debugging and analyzing performance simulations to suggest enhancements to Qualcomm hardware and software to tackle framework, compute and system memory-related bottlenecks
Successful applications will work in cross-site, cross-functional teams.