CerebrasSystems

Machine Learning Stack Engineer - Internship (PEY 2023)

Toronto, Ontario Canada
TensorFlow PyTorch Deep Learning Machine Learning Python
Description

Cerebras is developing 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 the deep learning workload.

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

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

  • Enrolled in the University of Toronto's PEY program with a degree in Computer Science, Computer Engineering, or other related disciplines
  • Strong proficiency in C/C++
  • Familiarity with Python or other scripting language
  • The ability to operate at multiple levels of abstraction in the software stack

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

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.


This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.

CerebrasSystems
CerebrasSystems
Artificial Intelligence Computer Hardware Software

0 applies

109 views

Other Jobs from CerebrasSystems

Distributed Software Engineer

Sunnyvale, CA San Diego, CA

Network Engineer

Sunnyvale, CA San Diego, CA

Senior Applied ML Engineer

Sunnyvale, CA San Diego, CA

There are more than 50,000 engineering jobs:

Subscribe to membership and unlock all jobs

Engineering Jobs

50,000+ jobs from 4,500+ well-funded companies

Updated Daily

New jobs are added every day as companies post them

Refined Search

Use filters like skill, location, etc to narrow results

Become a member

🥳🥳🥳 166 happy customers and counting...

Overall, over 80% of customers chose to renew their subscriptions after the initial sign-up.

Cancel anytime / Money-back guarantee

Wall of love from fellow engineers