ML Developer – Software Automation & Developer Infrastructure Intern

Cerebras has developed a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.

We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for deep learning workloads.

Cerebras is building a team of exceptional people to work together on big problems. Join us!

About The Role:As a Machine Developer - Software Automation & Developer Infrastructure Engineer, you will use your knowledge of testing and testability to influence better software design, promote proper engineering practice, bug prevention strategies, testability, scalability, and other advanced quality concepts. The position will play a huge role in the quality of Cerebras software. We are looking for engineers that have a broad set of technical skills and who are ready to tackle the biggest at-scale problems in HW-based deep learning accelerators.

Responsibilities:

• Write scripts to automate testing and create tools to allow easy development of software regression tests
• Help identify weak spots and potential customer pain points and drive the software organization towards customer focused quality metrics
• Implement creative ways to break software and identify potential problems
• Contribute to developing requirements specifications with a focus on developing verification tests

Requirements:

• Enrolled within University of Toronto’s PEY program with a degree in Computer Science, Computer Engineering, or any other related discipline
• Experience in developing automated tests for compute/machine learning or networking systems within a large-scale enterprise environment
• Ability to take responsibility for monitoring product development and usage at all levels with an end goal toward improving product quality
• Strong knowledge of software system design, C++ and Python

Preferred:

• Strong software testing experience with a proven track record in scaling highly technical teams
• Knowledge of UNIX/Linux and Windows environments
• Knowledge of neural network architecture and ML/AI deep learning principles
• Prior experience in designing and developing test automation for HW systems involving ASICs or FPGAs
• Prior experience working with live hardware systems and debug tools operating in a real time environment such as networking devices or live computing systems