At a Glance
- Tasks: Optimise CUDA kernels and improve GPU performance for cutting-edge AI systems.
- Company: Join a well-funded AI company at the forefront of technology.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting challenges and career advancement.
- Why this job: Make a real impact in the AI field while working with advanced technologies.
- Qualifications: Strong CUDA programming skills and experience with machine learning systems.
The predicted salary is between 48000 - 84000 € per year.
We're partnering with a well-funded AI company building advanced multi-agent systems. They're looking for engineers with experience working close to the GPU layer. This is a CUDA-heavy role focused on improving the performance of large-scale machine learning systems.
You'll be solving problems like:
- Optimising CUDA kernels for ML workloads
- Improving GPU utilisation and memory efficiency
- Profiling and debugging GPU bottlenecks
- Accelerating training and inference pipelines
- Working alongside ML researchers building multi-agent AI systems
Essentials:
- Strong CUDA / GPU programming experience
- Experience writing or optimising CUDA kernels
- Strong C++ and/or Python
- Experience working with machine learning systems
- Experience could come from LLMs, computer vision, speech, recommendation systems, or other ML domains.
Nice to have:
- PyTorch / TensorFlow internals
- Distributed training (NCCL, DeepSpeed, Megatron, Horovod, etc)
- GPU performance engineering or HPC background
Senior Software Engineer in Edinburgh employer: Fabrik Talent
Join a pioneering AI company that values innovation and collaboration, offering a dynamic remote work environment where your expertise in CUDA and GPU systems will directly contribute to cutting-edge multi-agent systems. With a strong focus on employee growth, you will have access to continuous learning opportunities and the chance to work alongside leading ML researchers, all while enjoying a culture that fosters creativity and teamwork.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Software Engineer in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and GPU space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Show off your skills! Create a GitHub repo with projects showcasing your CUDA and GPU programming prowess. We love seeing real-world applications of your work, especially if they relate to ML systems.
✨Tip Number 3
Prepare for technical interviews by brushing up on CUDA kernel optimisation and debugging techniques. We recommend doing mock interviews with friends or using platforms that focus on coding challenges to sharpen your skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate engineers ready to tackle exciting challenges in AI.
We think you need these skills to ace Senior Software Engineer in Edinburgh
Some tips for your application 🫡
Show Off Your CUDA Skills:Make sure to highlight your experience with CUDA and GPU programming in your application. We want to see how you've optimised kernels or improved performance in past projects, so don’t hold back!
Tailor Your Application:Take a moment to customise your CV and cover letter for this role. Mention specific experiences that relate to machine learning systems and how you’ve tackled challenges similar to those we face at StudySmarter.
Be Clear and Concise:When writing your application, keep it straightforward. We appreciate clarity, so make sure your points are easy to understand and directly relevant to the job description.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Fabrik Talent
✨Know Your CUDA Inside Out
Make sure you brush up on your CUDA programming skills. Be ready to discuss your experience with optimising CUDA kernels and how you've tackled performance issues in the past. Prepare specific examples that showcase your problem-solving abilities in GPU-heavy environments.
✨Showcase Your ML Knowledge
Since this role involves working closely with machine learning systems, be prepared to talk about your experience in this area. Whether it's LLMs, computer vision, or recommendation systems, have a few projects in mind that highlight your contributions and the impact they had on performance.
✨Familiarise Yourself with Tools and Frameworks
If you have experience with PyTorch, TensorFlow, or distributed training frameworks like NCCL or DeepSpeed, make sure to mention it. Understanding these tools can set you apart, so be ready to discuss how you've used them to enhance GPU performance or streamline training processes.
✨Prepare for Technical Challenges
Expect to face some technical questions or challenges during the interview. Practice coding problems related to CUDA and GPU optimisation. Being able to think on your feet and demonstrate your thought process will impress the interviewers and show that you're ready for the challenges of the role.
