Graduate Performance Engineer
Austin, United States · Full-time
- Posted 2w ago
- From Graphcore’s careers page
- Location
- Austin, United States
- Type
- Full-time
- Level
- Entry Level
- Experience
- 0+ years
- Department
- Engineering
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About the role
The Opportunity
As a Graduate Performance Engineer, you will contribute to the design, integration, bring-up, and validation of complex server- and rack-level performance of elaborate, large-scale systems. You will work alongside experienced engineers, software developer, and cross-functional partners while building practical skills in component, system and scale up/out performance optimization and design.
What You’ll Do
- Support the design, peer review, bring-up, and debug of complex server- and rack-level systems.
- Assist with modeling, design and evaluation of the complete software and hardware stack identifying performance bottlenecks, possible solutions and testing those outcomes.
- Collaborate with many different teams across both hardware and software development and testing.
What You’ll Bring
- A bachelor’s or master’s degree in electrical engineering, computer engineering, or a related discipline, completed before the role’s start date. Equivalent relevant education or practical experience will also be considered.
- Foundational knowledge of software development, hardware architecture and general understanding of performance implications.
- Hands-on experience gained through coursework, laboratories, internships, research, student projects, or personal projects.
- Ability to analyze technical problems, document your work, communicate clearly, and collaborate effectively.
- Curiosity, sound engineering judgment, and a willingness to learn in a fast-moving, multidisciplinary environment.
Preferred Qualifications
- Software and hardware architecture especially focused on performance implications
- Familiarity with high-speed interfaces such as PCI Express (PCIe), DDR memory, or Ethernet.
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About Graphcore
Processors and systems for machine learningGraphcore develops processors and systems designed to accelerate machine learning workloads.
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