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For Companies At a Glance
- Tasks: Develop and optimise CUDA kernels for cutting-edge chip development.
- Company: Join a dynamic start-up focused on innovative datacentre solutions.
- Benefits: Enjoy hybrid working options and a competitive salary based on experience.
- Why this job: Be part of a fast-growing team making an impact in high-performance computing.
- Qualifications: Strong C++ and CUDA skills, with experience in HPC environments required.
- Other info: Collaborate with cross-functional teams to enhance computational performance.
The predicted salary is between 36000 - 60000 £ per year.
IC Resources is seeking a CUDA Software Engineer to join a growing start-up with healthy runway, working on chip development for datacentre environments. You will be responsible for developing and optimising CUDA kernels and implementing efficient numerical algorithms for parallel processing. You will collaborate with cross-functional teams to improve computational performance and debug and troubleshoot complex CUDA-related issues.
What’s required for this CUDA Software Engineer position?
- Strong C++ and CUDA programming experience
- Experience with numerical optimization techniques
- Familiarity with GPU architecture
- Experience in HPC environments
If you are a CUDA Software Engineer looking for a new opportunity within a fast-growing, disruptive start-up, please apply to learn more.
What’s on offer:
- Excellent salary DOE.
- Hybrid working (2+ days onsite weekly).
CUDA Software Engineer employer: IC Resources
Contact Detail:
IC Resources Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land CUDA Software Engineer
✨Tip Number 1
Make sure to showcase your experience with C++ and CUDA programming in any conversations you have. Be prepared to discuss specific projects where you've optimised CUDA kernels or implemented numerical algorithms, as this will demonstrate your hands-on expertise.
✨Tip Number 2
Familiarise yourself with the latest developments in GPU architecture and high-performance computing (HPC) environments. Being able to speak knowledgeably about current trends and technologies can set you apart during discussions with the hiring team.
✨Tip Number 3
Network with professionals in the HPC and CUDA communities. Engaging in forums or attending relevant meetups can help you gain insights and potentially connect with someone at the company, which could give you an edge in the application process.
✨Tip Number 4
Prepare for technical interviews by practising problem-solving with CUDA-related challenges. Being able to demonstrate your troubleshooting skills and how you approach debugging complex issues will be crucial in showcasing your fit for the role.
We think you need these skills to ace CUDA Software Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong C++ and CUDA programming experience. Include specific projects or roles where you developed and optimised CUDA kernels, as this will directly relate to the job requirements.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for working in a start-up environment and mention your familiarity with GPU architecture and HPC environments. Use specific examples to demonstrate your skills and how they align with the company's goals.
Showcase Relevant Projects: If you have worked on any relevant projects, especially those involving numerical optimisation techniques or parallel processing, be sure to include them in your application. This can set you apart from other candidates.
Proofread Your Application: Before submitting your application, take the time to proofread it for any errors or typos. A well-presented application reflects your attention to detail, which is crucial for a technical role like this.
How to prepare for a job interview at IC Resources
✨Showcase Your CUDA Expertise
Be prepared to discuss your experience with CUDA programming in detail. Highlight specific projects where you've developed or optimised CUDA kernels, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Numerical Optimisation Skills
Since numerical optimisation techniques are crucial for this role, come equipped with examples of how you've applied these techniques in past projects. Discuss the impact your optimisations had on performance and efficiency.
✨Understand GPU Architecture
Familiarity with GPU architecture is essential. Brush up on the fundamentals and be ready to discuss how different architectures can affect performance. This knowledge will show your depth of understanding in the field.
✨Prepare for Problem-Solving Questions
Expect to face technical questions that test your problem-solving abilities, especially related to debugging and troubleshooting CUDA-related issues. Practice explaining your thought process clearly and logically.
