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- Tasks: Join a startup to optimise AI performance and tackle exciting challenges in machine learning.
- Company: A stealthy startup backed by top investors, focused on groundbreaking AI advancements.
- Benefits: Be part of a dynamic team with potential for remote work and innovative projects.
- Why this job: Shape the future of AI while collaborating with top talent in a diverse environment.
- Qualifications: Strong skills in Python, C++, or Rust; experience with GPU performance engineering and ML frameworks.
- Other info: Inclusive culture valuing diversity; applicants from 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 employer: Adamas Knight
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 the role. Familiarise yourself with profiling and debugging tools like NSight and CUDA GDB, as well as libraries such as Triton and cuDNN, to demonstrate your hands-on knowledge.
✨Tip Number 4
Research the latest trends and advancements in AI and machine learning, particularly in foundational model architecture and efficient training. Being able to discuss these topics during your interview will show your genuine interest and understanding of the field.
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 relevant experience in Python, C++, or Rust, as well as any work with GPU performance engineering. Emphasise your familiarity with ML frameworks like PyTorch and profiling tools such as NSight.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and your hacker's curiosity. Discuss specific projects where you've optimised performance or tackled complex systems, showcasing your problem-solving skills.
Showcase Your Technical Skills: Include specific examples of your experience with CUDA, PTX/SASS, and libraries like Triton or cuDNN. Mention any collaborative projects that demonstrate your ability to work across teams and contribute to open science.
Highlight Your Mindset: Convey your product intuition and collaborative spirit in your application. Share instances where your work had a real-world impact, and how you approach challenges with a mindset geared towards transparency and integrity.
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 Problem-Solving Abilities
Expect technical questions that assess your understanding of performance bottlenecks. Be ready to explain how you would approach issues like memory bandwidth ceilings or kernel launch inefficiencies, showcasing your analytical mindset.
✨Foster a Collaborative Spirit
Emphasise your ability to work across different teams, such as research and infrastructure. Share examples of past collaborations that led to successful outcomes, as this aligns with the company's value of teamwork.
✨Express Your Curiosity and Passion
Convey your enthusiasm for AI and machine learning. Discuss any personal projects or research that reflect your hacker’s curiosity and commitment to pushing boundaries in technology, which will resonate well with the startup's ambitious goals.
