Member of Technical Staff

Machine Learning EngineerRemote Europe | Up to €150,000 + EquityI am working with a well backed AI startup building infrastructure for the next generation of production AI systems.They are solving a very real problem in the AI market. Companies are using large general purpose LLMs in production, but for repeatable tasks these models can be expensive, slow and difficult to control.This team is building a platform that trains smaller, task specific language models that can match frontier model quality on narrow tasks, while reducing cost and latency.They are backed by one of Europe’s leading AI investors and already work with customers across defence, cybersecurity, robotics and education.Why this one stands outUp to €150,000 plus equityRemote across European time zonesEarly team, real ownership and strong career progressionBacked by one of Europe’s leading AI investorsWorking on a fundamental AI infrastructure problemBuilding systems around small language models, synthetic data, fine tuning, evaluation and inferenceRegular team offsites in EuropeThe RoleThis is a hands on Machine Learning Engineer role sitting across ML infrastructure, model training, backend systems and low latency inference.You will help build the full production pipeline behind task specific language models, from synthetic data generation and fine tuning through to evaluation, serving, monitoring and deployment.It is not a pure research role and it is not a pure backend role. The strongest fit will be someone who can build real ML systems around models and understands how to take them into production.What you will work onBuild and improve infrastructure for synthetic data generation, model training and evaluationWork on scalable orchestration for GPU jobs using Kubernetes, Argo Workflows or similarRun and optimise fine tuning workloads using PyTorch, Hugging Face, LoRA, DDP, FSDP or similarBuild high throughput teacher model inference pipelinesDevelop validation and filtering systems to keep synthetic training data high qualityBuild secure, multi tenant model serving infrastructure for production workloadsWork on low latency inference, auto scaling, observability and cost monitoringPartner closely with ML scientists on knowledge distillation, synthetic data generation and model evaluationHelp turn research into reliable customer facing systemsWhat they are looking forStrong Python engineering skillsExperience building ML, data or backend infrastructure at scaleHands on experience with model training, fine tuning or distributed ML workloadsExperience with PyTorch, Hugging Face, JAX, TensorFlow or similarGood understanding of Kubernetes, workflow orchestration or distributed computeExperience with Docker, cloud infrastructure and infrastructure as codeExposure to model serving, inference optimisation or production ML systemsStrong problem solving skills and a bias towards automation and reliabilityComfortable working in a small, fast moving technical teamWho this could suitThis could suit someone from an ML infrastructure, ML platform, LLM infrastructure, research engineering or applied ML engineering background.You might be someone who has worked across training, evaluation, serving and deployment, and now wants more ownership in a smaller, highly technical AI company.The strongest fit will be someone who enjoys building real systems around models and cares about making AI cheaper, faster and reliable enough for production.The practicalitiesRemote across European time zonesRegular team offsites in EuropeUp to €150,000 plus equityEarly team with strong technical ownershipBacked by one of Europe’s leading AI investors