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- Tasks: Join a startup tackling exciting AI challenges and shape its technical future.
- Company: A bold startup backed by top investors, focused on AI innovation.
- Benefits: Be part of a pioneering team with potential for remote work and growth opportunities.
- Why this job: Make a real-world impact in AI while collaborating with top talent.
- Qualifications: Strong skills in Python, C++, or Rust; GPU performance engineering experience required.
- Other info: Diversity is valued; all backgrounds are encouraged to apply.
The predicted salary is between 48000 - 84000 £ per year.
We are recruiting on behalf of an ambitious new startup founded by an exceptional team of ex-big tech researchers and engineers. Based between London and SF, they’ve recently raised over $15M in pre-seed funding from world-class investors and are building a technical founding team to take on some of the hardest and most exciting challenges in AI today. The company is still in stealth, but their focus is bold and clear: pushing the boundaries of foundational model architecture, efficient training at scale, and real-world deployment of intelligent agents. This is a rare opportunity to join early and shape the technical DNA of a company that is making a major mark in the future of AI/AGI.
What We’re Looking For
- Technical Experience
- Strong engineering skills in Python, C++, or Rust
- Proven experience with GPU performance engineering: CUDA, PTX/SASS, Tensor Cores, memory hierarchy, warp-level primitives
- Familiarity with ML frameworks like PyTorch, and their internals
- Proficiency in profiling and debugging tools like NSight, CUDA GDB, nvprof, NSight Compute
- Deep knowledge of Triton, cuDNN, cuBLAS, CUTLASS, CUB, or similar libraries
- Experience optimising across the stack: from kernel-level compute to cluster-wide networking and memory IO
- Background in distributed systems or HPC: understanding of Infiniband, NVLink, RoCE, GPUDirect, NCCL, MPI
- Experience with multi-node training, collective communication algorithms, and throughput analysis
- Comfort navigating complex systems to answer questions like: “Is this a memory bandwidth ceiling or a kernel launch inefficiency?”
- A hacker’s curiosity: you love breaking things down and figuring out how to make them faster
- Product intuition: performance isn’t abstract to you, it’s about real-world impact
- Collaborative spirit: you’re excited to work across research, infra, and open-source teams
- A bias toward open science, transparency, and high-integrity work
At Adamas Knight, we are committed to creating an inclusive culture. We do not discriminate based on race, religion, gender, national origin, sexual orientation, age, veteran status, disability, or any other legally protected status. Diversity is highly valued, and we encourage applicants from all backgrounds to apply.
Machine Learning Performance Engineer (London Area) employer: Adamas Knight
Contact Detail:
Adamas Knight Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Performance Engineer (London Area)
✨Tip Number 1
Network with professionals in the AI and machine learning community. Attend meetups, webinars, or conferences where you can connect with people who work in similar roles. This can help you gain insights into the company culture and potentially get a referral.
✨Tip Number 2
Showcase your technical skills through personal projects or contributions to open-source initiatives. Having a portfolio that demonstrates your expertise in Python, C++, or Rust, as well as your experience with GPU performance engineering, can set you apart from other candidates.
✨Tip Number 3
Familiarise yourself with the latest advancements in AI and machine learning frameworks like PyTorch. Being able to discuss recent developments or challenges in these areas during an interview can demonstrate your passion and commitment to the field.
✨Tip Number 4
Prepare to discuss your problem-solving approach in detail. Be ready to explain how you've tackled complex issues in previous roles, especially those related to performance optimisation and distributed systems. This will showcase your analytical mindset and collaborative spirit.
We think you need these skills to ace Machine Learning Performance Engineer (London Area)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in Python, C++, or Rust, as well as your familiarity with GPU performance engineering and ML frameworks like PyTorch. Use specific examples to demonstrate your skills.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company’s mission. Discuss your hacker's curiosity and collaborative spirit, and how these traits align with the company's focus on pushing AI boundaries.
Showcase Technical Skills: Include specific projects or experiences that showcase your proficiency with profiling and debugging tools, as well as your knowledge of libraries like Triton and cuDNN. This will help you stand out as a candidate.
Highlight Systems Fluency: Demonstrate your understanding of distributed systems or HPC in your application. Mention any relevant experience with multi-node training or collective communication algorithms to show you can navigate complex systems effectively.
How to prepare for a job interview at Adamas Knight
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, C++, or Rust in detail. Highlight specific projects where you optimised GPU performance using CUDA or similar technologies, as this will demonstrate your hands-on expertise.
✨Demonstrate Systems Fluency
Familiarise yourself with distributed systems concepts and be ready to explain how you've tackled challenges related to multi-node training or memory bandwidth issues. This will show that you can navigate complex systems effectively.
✨Exhibit a Hacker's Curiosity
Prepare examples of how you've approached problems with a curious mindset. Discuss instances where you broke down a system to improve its performance, as this aligns with the company's values and expectations.
✨Emphasise Collaboration
Since the role involves working across various teams, share experiences where you collaborated with others in research or open-source projects. Highlight your ability to communicate technical concepts clearly to non-technical team members.
