| JOB POSTING INFORMATION | |||||||
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| Position Type: | Professional Experience Year Co-op (PEY Co-op: 12-16 months) | ||||||
| Job Title: | Machine Learning Stack Engineer | ||||||
| Job Location: | Toronto | ||||||
| Job Location Type: | Flexible | ||||||
| If working on site, can you provide a copy of your COVID-19 safety protocols?: | No | ||||||
| Number of Positions: | 1 | ||||||
| Salary: | $42.00 hourly for 40.0 hours per week | ||||||
| Start Date: | 05/06/2024 | ||||||
| End Date: | 04/25/2025 | ||||||
| Job Function: | Information Technology (IT) | ||||||
| Job Description: |
Cerebras Systems has pioneered a groundbreaking chip and system that revolutionizes deep learning applications. Our system empowers ML researchers to achieve unprecedented speeds in training and inference workloads, propelling AI innovation to new horizons. The Condor Galaxy 1 (CG-1), unveiled in a recent announcement, stands as a testament to Cerebras' commitment to pushing the boundaries of AI computing. With a staggering 4 ExaFLOP processing power, 54 million cores, and 64-node architecture, the CG-1 is the first of nine powerful supercomputers to be built and operated through an exclusive partnership between Cerebras and G42. This strategic collaboration aims to redefine the possibilities of AI by creating a network of interconnected supercomputers that will collectively deliver a mind-boggling 36 ExaFLOPS of AI compute power upon completion in 2024. Cerebras is building a team of exceptional people to work together on big problems. Join us! 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 |
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| Job Requirements: |
Requirements
Enrolled within University of Toronto's PEY program with a degree in Computer Science, Computer Engineering, or any 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 |
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| Preferred Disciplines: |
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| All Co-op programs: | No | ||||||
| Targeted Co-op Programs: |
Targeted Programs
Professional Experience Year Co-op (12 - 16 months)
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| APPLICATION INFORMATION | |
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| Application Deadline: | Nov 1, 2023 11:59 PM |
| Application Receipt Procedure: | Online via system |
| Additional Application Information: |
Please apply with both resume & transcript. Lacking transcript will disqualify you from being considered. Note that applications will be considered on a rolling basis. Apply as early as possible. |
| U of T Job Coordinator: | Yasmine Abdelhady |
| ORGANIZATION INFORMATION | |
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| Organization: | Cerebras Systems |
| Division: | Computer Science |
| Website: | https://cerebras.net/ |
| ADDITIONAL INFORMATION | |
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| Length of Workterm: | FLEXIBLE PEY Co-op: 12-16 months (range) |