Artificial Intelligence Engineer - Distributed Inference

Danucore
Birmingham
4 months ago
Applications closed

Related Jobs

View all jobs

Computer Vision and Artificial Intelligence Engineer

Senior Machine Learning Engineer

Deep Learning Engineer

Associate Director of AI

AI Research Engineer, PhD

Machine Learning Engineer

AI Engineer - Distributed Inference Specialist


Do you want to be a spectator or a player as the world races to develop AGI?

Are you ready to be a pioneer of AI?


Why join us?


AtDanucore, we are on the hunt forBRILLIANT MINDSto join a team of visionaries and innovators dedicated to building distributedsupercomputersandAI systemswhich are:


Faster️ –from building and deploying AI datacentres at speed to optimising the AI workloads that run on them we want to be the fastest


CheaperAI should be accessible to all. We lower the costs of AI deployment with careful hardware deployments and software systems to ensure efficient resource utilisation.


KinderOur systems are designed to benefit humanity. We do not allow our systems to participate in military, gambling or pornography applications


GreenerWe optimise energy consumption with an integrated hardware and software solution to leverage renewable energy, optimise heat recovery - all running under energy aware orchestration systems to optimise workloads


ClevererWe develop agentic AI systems and to make our systems intelligent and constantly improving


Help us build systems to ensure the power of frontier AI remainsaccessibleand give userssovereigntyover their AI systems


Join us in ensuring that the most transformative technology in human history remains in the hands of humanity itself. Let's make AI development transparent, accessible, and aligned with the interests of humanity, not just the profits of a few. ⚡


About the Role


This role is for those obsessed with pushing the boundaries of AI model performance.


We're looking for someone who gets excited about shaving milliseconds off inference time, every percentage point of GPU utilization gained and how many Watts were consumed to achieve it. ⚡️


You'll work directly with cutting-edge models — from LLMs to multimodal systems — and large GPU clusters, finding innovative ways to make them run faster, more efficiently, and more accessibly on diverse hardware setups. ️


What We're Looking For


In team members:


  • Passion for AI: A strong desire to influence the future of technology and its societal impact.
  • Willingness to Learn: we're looking for future experts with curious minds and a growth mindset.
  • Open-Mindedness: Ready to challenge the norm and think outside the box?


and for the role:


  • Evidence of deploying and optimising AI models in multi gpu and multi node systems ️ ️
  • Good working knowledge of leading AI runtimes: PyTorch, vLLM, TensorRT, ONNX Runtime, Llama.cpp ‍♂️‍➡️⏱️o
  • Experience with distributed inference engines: Ray Serve, Triton Inference Server, vLLM, SLURM
  • Knowledge of AI compilers: OpenXLA, torch.compile, OpenAI's triton, MLIR, Mojo, TVM, MLC-LLM ⚙️
  • Good working knowledge of inter-process communication: message queues, MPI, NCCL, gRPC
  • Good working knowledge of high performance networking: RDMA, RoCE, Infiniband, NVIDIA GPUDirect, NVLink, NVIDIA DOCA, MagnumIO, dpdk, spdk
  • Experience with model quantisation, pruning, and sparsity techniques for performance optimisation.


And bonus points if you have:

  • a homelab, blog, or a collection of git repos showcasing your talents and interests ‍ ‍
  • made contributions to open-source projects or publications in the field of AI/ML systems optimisation


Let us know which of the above you have worked with / are relevant in your cover letter! ✨


Key Responsibilities


  • Design and implement high-performance distributed inference systems for running large language models and multimodal AI models at scale
  • Optimise model serving infrastructure for maximum throughput, minimal latency, and optimal power efficiency ⚡
  • Develop and maintain deployment pipelines for efficient model serving, and monitoring in production
  • Research and implement cutting-edge techniques in model optimisation, including pruning, quantisation, and sparsity methods ‍
  • Design, build and configure experimental hardware setups for model serving and optimisation ️
  • Design and implement robust testing frameworks to ensure reliable model serving ✅
  • Collaborate with the team to build and improve our distributed inference platform, making it more accessible and efficient for users
  • Monitor, optimise and document system performance metrics, including latency, throughput, power consumption and benchmark scores



How Can We Tempt You?


Exceptional Financial Package: Enjoy a competitive compensation structure, including an enticing EMI scheme that rewards your brilliance.


Envious Compute Power: Gain access to a vast array of cutting-edge computing resources to bring your ideas to life!


Support for Your Vision: We believe that the brightest minds often have their own innovative projects. Let's collaborate! Share your ideas, and work with our team and support network to make them happen!


Make an Impact: Join a passionate team dedicated to creating positive change in the world. The future is ours to shape, and together we can ensure it's for the better.


Dynamic Start-Up Culture: Dive in from day one! Experience the thrill of a start-up environment where you can roll up your sleeves and make a real difference right away.



How to Apply

Email your cover letter and CV to with subject "AI Engineer - Distributed Inference"

In your cover letter, please include details of:

  • what parts or technologies mentioned in this job advert you have experience with and can add value with
  • links to any public work e.g. github profile, blogs or papers

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Top 10 Mistakes Candidates Make When Applying for Machine-Learning Jobs—And How to Avoid Them

Landing a machine-learning job in the UK is competitive. Learn the 10 biggest mistakes applicants make—plus tested fixes, expert resources and live links that will help you secure your next ML role. Introduction From fintechs in London’s Square Mile to advanced-research hubs in Cambridge, demand for machine-learning talent is exploding. Job boards such as MachineLearningJobs.co.uk list new vacancies daily, and LinkedIn shows more than 10,000 open ML roles across the UK right now. Yet hiring managers still reject most CVs long before interview—often for avoidable errors. Below are the ten most common mistakes we see, each paired with a practical fix and a live resource link so you can dive deeper.

Top 10 Best UK Universities for Machine Learning Degrees (2025 Guide)

Explore ten UK universities that deliver world-class machine-learning degrees in 2025. Compare entry requirements, course content, research strength and industry links to find the programme that fits your goals. Machine learning (ML) has shifted from academic curiosity to the engine powering everything from personalised medicine to autonomous vehicles. UK universities have long been pioneers in the field, and their programmes now blend rigorous theory with hands-on practice on industrial-scale datasets. Below, we highlight ten institutions whose undergraduate or postgraduate pathways focus squarely on machine learning. League tables move each year, but these universities consistently excel in teaching, research and collaboration with industry.

How to Write a Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.