Responsibilities:
- Write safe, scalable, modular, and high-quality (C++/Python) code for our core backend software.
- Perform benchmarking, profiling, and system-level programming for GPU applications.
- Provide code reviews, design docs, and tutorials to facilitate collaboration among the team.
- Conduct unit tests and performance tests for different stages of the inference pipeline.
Who you are:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
- Strong coding skills in Python and C/C++.
- 5+ years of industry experience in software engineering.
- Knowledgeable and passionate about machine learning and performance engineering.
Nice to haves:
- Solid fundamentals in machine learning and deep learning.
- Solid fundamentals in operating systems, computer architecture, and parallel programming.
- Research experience in systems or machine learning.
- Industry experience in building enterprise-scale large distributed systems.
- Experience with training, deploying, or optimizing the inference of LLMs in production is a plus.
- Experience with performance modeling, profiling, debugging, and code optimization or architectural knowledge of CPU and GPU is a plus.
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What We Do
We pioneer novel technology to enhance computing efficiency, making AI accessible for innovation and to benefit the global community.
We believe honesty builds integrity, honing craftsmanship delivers excellence, and collaboration fosters community.
Why Work With Us
Our journey began in the esteemed Efficient Computing Systems lab at the University of Toronto, under the leadership of our CEO, Gennady Pekhimenko. Today, the EcoSystems lab stands proudly as one of the world’s foremost authorities in Machine Learning Systems.
Our founding team is made up of experts in AI, ML compilers and ML hardware and has led
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