At a Glance
- Tasks: Optimise GPU performance and enhance training efficiency across massive GPU clusters.
- Company: Join a cutting-edge AI company backed by NVIDIA, collaborating with top tech minds.
- Benefits: Competitive salary, meaningful equity, full visa sponsorship, and relocation support.
- Other info: Exciting startup environment with opportunities for growth and collaboration.
- Why this job: Be at the forefront of AI innovation and make a significant impact on technology.
- Qualifications: Deep experience in GPU infrastructure and distributed systems is essential.
The predicted salary is between 70000 - 90000 € per year.
Join a frontier AI company backed by NVIDIA, building large-scale open-weight foundation models alongside researchers and engineers from DeepMind, OpenAI, Meta, Anthropic, and Google Brain.
What You'll Do
- Optimise GPU performance and training efficiency across 1,000+ GPU clusters
- Improve utilisation, throughput, and reliability across distributed training infrastructure
- Build tooling for orchestration, monitoring, scheduling, and observability
- Work closely with research teams to accelerate large-scale model training
What They're Looking For
- Deep GPU infrastructure / distributed systems experience
- Strong knowledge of CUDA, NCCL, PyTorch, DeepSpeed, JAX, Megatron-LM, vLLM, etc.
- Experience operating large-scale GPU clusters (1,000+ GPUs)
- Kubernetes, Slurm, or similar orchestration expertise
- BONUS: Experience working on NVIDIA Blackwell chips (B200, B300, GB200, GB300)
Package
- Salary open to candidate expectations
- Meaningful startup equity
- Full visa sponsorship + relocation support
Software Engineer employer: MindMatch
Join a pioneering AI company in London that offers an exceptional work environment, fostering innovation and collaboration with top-tier researchers and engineers. With competitive salaries, meaningful startup equity, and comprehensive visa sponsorship and relocation support, this role provides not only a chance to work on cutting-edge technology but also ample opportunities for professional growth and development in a vibrant city known for its tech scene.
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those who work at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to GPU infrastructure or distributed systems. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges and system design questions. Use platforms like LeetCode or HackerRank to sharpen your skills. Remember, they want to see how you think and solve problems!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Software Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with GPU infrastructure and distributed systems. We want to see how your skills align with what we're looking for, so don’t be shy about showcasing your knowledge of CUDA, PyTorch, and other relevant tech.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you're excited about the role and how your background makes you a perfect fit. We love hearing about your passion for AI and large-scale model training, so let that enthusiasm come through!
Showcase Relevant Projects:If you've worked on any projects involving large-scale GPU clusters or orchestration tools like Kubernetes, make sure to mention them. We’re keen to see real-world applications of your skills, so include links or descriptions of your work.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company and culture!
How to prepare for a job interview at MindMatch
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of GPU infrastructure and distributed systems. Be ready to discuss your experience with CUDA, NCCL, and any relevant frameworks like PyTorch or DeepSpeed. The more specific examples you can provide about optimising performance or managing large-scale clusters, the better!
✨Showcase Your Problem-Solving Skills
Prepare to tackle some technical challenges during the interview. Think about past projects where you improved GPU utilisation or reliability. Be ready to explain your thought process and how you approached these problems, as this will demonstrate your analytical skills and ability to work under pressure.
✨Familiarise Yourself with Their Tools
Since the role involves orchestration and monitoring, it’s a good idea to get comfortable with tools like Kubernetes or Slurm. If you have experience with any specific tools they use, make sure to highlight that. Showing that you can hit the ground running will definitely impress them!
✨Connect with Their Mission
Research the company’s goals and their work in AI. Understanding their vision and how your role contributes to it can set you apart. Be prepared to discuss how your background aligns with their mission and how you can help accelerate large-scale model training.
