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For Companies At a Glance
- Tasks: Join a startup to tackle exciting AI challenges and optimise machine learning performance.
- Company: A stealthy startup backed by top investors, focused on groundbreaking AI advancements.
- Benefits: Work in a dynamic environment with potential for remote options and innovative projects.
- Why this job: Shape the future of AI while collaborating with top-tier engineers and researchers.
- Qualifications: Strong skills in Python, C++, or Rust; experience with GPU performance engineering and ML frameworks.
- Other info: Diversity is celebrated; all backgrounds are encouraged to apply.
The predicted salary is between 48000 - 72000 £ 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.
Contact Detail:
Adamas Knight Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Performance Engineer
✨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
Prepare for technical interviews by practising problem-solving and optimisation scenarios relevant to machine learning performance. Familiarise yourself with profiling and debugging tools like NSight and CUDA GDB, as these are likely to come up during discussions.
✨Tip Number 4
Research the latest trends and advancements in AI and foundational model architecture. Being knowledgeable about current challenges and innovations in the field will not only impress your interviewers but also demonstrate your genuine interest in the company's mission.
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your technical skills in Python, C++, or Rust, and showcases your experience with GPU performance engineering. Include specific projects or achievements that demonstrate your familiarity with ML frameworks like PyTorch.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for AI and your understanding of the challenges in foundational model architecture. Mention your hacker's curiosity and collaborative spirit, and how these traits align with the company's mission.
Showcase Relevant Experience: In your application, emphasise your background in distributed systems or HPC. Provide examples of your work with multi-node training and collective communication algorithms to illustrate your expertise.
Highlight Your Mindset: Convey your product intuition and commitment to open science in your application. Discuss how you approach problem-solving and your excitement about contributing to a culture of inclusivity and diversity within the company.
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've optimised GPU performance using CUDA or similar technologies, as this will demonstrate your technical prowess.
✨Demonstrate Problem-Solving Abilities
Expect to face technical challenges during the interview. Practice explaining your thought process when troubleshooting issues related to memory bandwidth or kernel inefficiencies, as this reflects your analytical mindset.
✨Familiarise Yourself with ML Frameworks
Brush up on your knowledge of ML frameworks like PyTorch and their internals. Be ready to discuss how you’ve used profiling and debugging tools such as NSight or CUDA GDB in past projects to enhance performance.
✨Emphasise Collaboration and Curiosity
This role values a collaborative spirit and a hacker's curiosity. Prepare examples of how you've worked across teams or contributed to open-source projects, showcasing your ability to communicate and innovate within a team environment.
