> Markdown version of [/jobs/ext/97216-staff-principal-machine-learning-engineer-serving](https://www.wearedevelopers.com/jobs/ext/97216-staff-principal-machine-learning-engineer-serving). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff / Principal Machine Learning Engineer, Serving - **Company:** Inworld AI - **Location:** Liverpool, UK - **Salary:** £140,000.0 - £200,000.0 - **Contract:** Permanent contract - **Skills:** C++ (Programming Language), Nvidia CUDA, Distributed Systems, Python (Programming Language), Machine Learning, System Programming, Rust (Programming Language), Graphics Processing Unit (GPU), Load Balancing, Large Language Models, Caching, Backend, Kubernetes, Free and Open-Source Software, Machine Learning Operations - **Published:** May 17, 2026 - **Apply:** https://find.jobs/jobs-near-me/staff-principal-machine-learning-engineer-serving-liverpool/2773058786-2/ ## About the Role A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template - we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. * Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. * Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. * High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. * Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. * Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. * Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. * Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. Who Thrives Here * You don't need a roadmap to start walking; you're comfortable picking a direction and building the map as you go. * You believe engineering isn't finished until it's shipped and stable. You have a bias for impact over purely theoretical optimizations. * You don't just ship code; you obsess over the why. You're the first to question an architecture if you think there's a better way to solve the core latency or throughput problem. * You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Event based cache invalidation in GraphQL](https://www.wearedevelopers.com/videos/433-event-based-cache-invalidation-in-graphql) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Never delegate the understanding](https://www.wearedevelopers.com/magazine/749-never-delegate-the-understanding)