Machine Learning Stack Engineer - New Grad

Cerebras Toronto

Cerebras is based in Sunnyvale, California, with its second engineering hub – the AI Centre of Excellence – located in Downtown Toronto. Toronto houses our Machine Learning and Software organization and has some of the most talented ML, optimization, and high-performance computing talent in the world. We have already built out an experienced team of over 50 engineers and computer scientists that are driving forward the next generation of our machine learning stack.

About the Role

As a ML Stack Engineer, you will directly impact the performance at which deep learning models are executed on hardware and be responsible for enabling next-generation AI applications that require substantial computational capabilities. In this position, you will develop algorithms for compilation, execution, acceleration, partitioning, placement, floor planning, and routing of communication for dataflow graphs on a massively parallel, multi-core architecture.

Specific responsibilities may include:

• Develop algorithms for allocation of compute, communication, and memory resources
• Implement mathematical models in C++ or Python using discrete optimization techniques and standard libraries and packages
• Measure, analyze, and improve optimization passes/algorithms
• Integrate successful optimizations into production software stack

Requirements

• Completed or completing degree in Computer Science, Computer Engineering, or other related disciplines
• Strong proficiency in C/C++ or other language for designing large, performant systems
• Familiarity with Python or other scripting language
• Ability to operate at multiple levels of abstraction in the software stack
• Demonstrated ability to both solve challenging algorithmic problems and implement them cleanly in performant code

Preferred

• Familiarity with compiler technology (LLVM, MLIR)
• Familiarity with TensorFlow and PyTorch internals
• Knowledge of linear programming, constraint solvers, and combinatorial optimization
• Experience modeling optimization problems using simulated annealing, genetic programming, and dynamic programming