National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Machine Learning Performance Engineer

Adamas Knight
Slough
1 week ago
Applications closed

Related Jobs

View all jobs

Applied Scientist II (Machine Learning), ITA - Automated Performance Evaluation

Principal Data Engineer

Principal Data Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - Up to £150k + Equity

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 major mark in the future of AI/AGI.


What We’re Looking For

There’s no checklist, but you’ll likely thrive in this role if you have:


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


Systems Fluency

  • 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?”


Your Mindset

  • 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.

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.