Member of Technical Staff

Machine Learning Engineer

Remote Europe | Up to €150,000 + Equity

I 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 out

• Up to €150,000 plus equity
• Remote across European time zones
• Early team, real ownership and strong career progression
• Backed by one of Europe’s leading AI investors
• Working on a fundamental AI infrastructure problem
• Building systems around small language models, synthetic data, fine tuning, evaluation and inference
• Regular team offsites in Europe

The Role

This 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 on

Build and improve infrastructure for synthetic data generation, model training and evaluation

Work on scalable orchestration for GPU jobs using Kubernetes, Argo Workflows or similar

Run and optimise fine tuning workloads using PyTorch, Hugging Face, LoRA, DDP, FSDP or similar

Build high throughput teacher model inference pipelines

Develop validation and filtering systems to keep synthetic training data high quality

Build secure, multi tenant model serving infrastructure for production workloads

Work on low latency inference, auto scaling, observability and cost monitoring

Partner closely with ML scientists on knowledge distillation, synthetic data generation and model evaluation

Help turn research into reliable customer facing systems

What they are looking for

• Strong Python engineering skills
• Experience building ML, data or backend infrastructure at scale
• Hands on experience with model training, fine tuning or distributed ML workloads
• Experience with PyTorch, Hugging Face, JAX, TensorFlow or similar
• Good understanding of Kubernetes, workflow orchestration or distributed compute
• Experience with Docker, cloud infrastructure and infrastructure as code
• Exposure to model serving, inference optimisation or production ML systems
• Strong problem solving skills and a bias towards automation and reliability
• Comfortable working in a small, fast moving technical team

Who this could suit

This 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 practicalities

• Remote across European time zones
• Regular team offsites in Europe
• Up to €150,000 plus equity
• Early team with strong technical ownership
• Backed by one of Europe