AI Research Engineer - ML Engineering

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process. *If you're interested sign up to Dex to apply.* Dex is an AI recruiter agent that helps you run your job search. Tell Dex your stack, seniority, and what you want to build. We will manage your applications and surface other opportunities that are a fit. The role A company building AI to protect democracies. They're focused on achieving technological leadership to ensure open societies can make sovereign decisions. Rapidly growing across Europe, they're backed by leading investors and founded by engineers from top tech companies. You'll join a cross-functional team at the convergence of machine learning and systems engineering. This isn't a pure research role, nor is it just infrastructure. You'll build the core tools and platforms that enable breakthroughs in high-volume data processing, RL agent training, and large-scale foundation models. Your scope: abstracting complex distributed systems to maximise training throughput and developer velocity, ensuring GPUs stay warm and productive. The work * Extend highly integrated deep learning frameworks (built on PyTorch) for efficiency and ease of use across diverse applications. * Scale infrastructure and tooling to support faster, larger distributed training. * Design data strategies for large-scale datasets, focusing on efficient storage and access. * Debug complex production ML pipelines, identifying and resolving subtle numerical or performance issues. * Rapidly integrate the latest AI optimisation techniques and research into core codebases. What You Bring * MSc or PhD in Computer Science or a STEM field, with a deep focus on machine learning and deep learning. * Strong Python engineering skills. You don't just use DL frameworks (PyTorch, JAX, TensorFlow); you build custom layers, loss functions, and distributed training loops. * A track record of debugging production ML pipelines, solving subtle numerical or performance issues. * A first-principles mindset, eager to integrate cutting-edge AI optimisation research into practical code. * Ideal candidates will also have hands-on experience with large-scale GPU cluster training (NCCL, MPI), multi-modal data strategies, workload orchestrators (Slurm, Kubernetes, Ray), or low-level GPU architecture. Why apply through Dex This is a rare role at a company that doesn't advertise widely. Applying through Dex means you skip the cold application process and get a full brief on the company and role before you even interview. We help you cut through the noise and get noticed for opportunities like this. If you're interested, sign up to Dex to apply - https://jobs.meetdex.ai/jobs/d0106735-166b-4929-a4fa-bd92902b3c1a *As part of the recruitment process at Dex, we process your personal data in accordance with our Privacy Notice for Job Applicants. This notice explains how and why your data is collected and used, and how you can contact us if you have any concerns.*