At a Glance
- Tasks: Optimise AI training performance and collaborate with researchers on innovative projects.
- Company: Cohere, a leader in AI technology with a focus on collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic work environment with excellent career advancement opportunities.
- Why this job: Join a cutting-edge team and make a real impact in AI language understanding.
- Qualifications: Experience in CUDA/Triton and knowledge of distributed training strategies.
The predicted salary is between 63000 - 77000 £ per year.
Cohere is seeking a Performance Engineer for the Pre-Training team to optimize language model training and system performance.
You will focus on throughput, accelerator utilization, and robust tooling, collaborating with researchers and engineers across teams.
The role emphasizes low-level kernel work (CUDA/Triton), distributed training strategies, and cutting-edge ML infrastructure to drive efficient language understanding and generation.
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Performance Engineer for Scalable AI Training employer: Visa Hunt
Gartner is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation and collaboration thrive. With a strong emphasis on employee growth, you will have access to mentorship opportunities and resources to advance your career while working in a hybrid model that promotes work-life balance. Located in a vibrant market, Gartner offers the unique advantage of engaging with top executives globally, making your contributions impactful and rewarding.
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We think this is how you could land Performance Engineer for Scalable AI Training
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We think you need these skills to ace Performance Engineer for Scalable AI Training
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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