Hardware-Aware AI Research Engineer

Hardware-Aware AI Research Engineer

Full-Time 80000 - 100000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Advance AI research and develop hardware-aware ML methods with a focus on scalable implementations.
  • Company: Join Graphcore, a leader in AI hardware innovation with a collaborative culture.
  • Benefits: Competitive salary, exposure to cutting-edge technology, and opportunities for impactful publications.
  • Other info: Collaborate with top researchers across London, Cambridge, and Bristol.
  • Why this job: Be at the forefront of AI research and make a real difference in the tech world.
  • Qualifications: Strong software engineering skills and experience in performance tuning.

The predicted salary is between 80000 - 100000 Β£ per year.

Graphcore seeks an innovative research engineer to advance AI research and develop hardware-aware ML methods.

You will translate ideas into scalable implementations, working with researchers and engineers across our London, Cambridge and Bristol sites.

We value strong software engineering skills, performance tuning, and the ability to publish impactful work at leading conferences.

This role offers exposure to cutting-edge AI hardware and a collaborative research culture. #J-18808-Ljbffr

Hardware-Aware AI Research Engineer employer: Cerebras

Graphcore is an exceptional employer located in the vibrant city of Bristol, offering a dynamic work culture that fosters innovation and collaboration. Employees benefit from competitive salaries, flexible working arrangements, and generous leave policies, alongside opportunities for professional growth in cutting-edge AI technology. With a focus on employee well-being, including private medical insurance, Graphcore stands out as a rewarding place to build a meaningful career.

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Contact Details:

Cerebras Recruitment Team

We think you need these skills to ace Hardware-Aware AI Research Engineer

AI Research
Machine Learning Methods
Software Engineering Skills
Performance Tuning
Scalable Implementations
Collaboration
Research Publication