About the role
At Substrate AI, we build AI solutions and infrastructure for European businesses. Our Serenity platform brings AI into companies through intelligent agents that operate directly within the business, increasingly powered by our own distributed GPU infrastructure in Spain — Serenity Cloud. We are now looking for a Founding LLM Engineer to build and lead our LLM Lab.
This is a unique opportunity to take ownership of the models powering Serenity's agents: selecting, fine-tuning, evaluating, quantizing and optimizing domain-specific language models while working directly with our GPU infrastructure. You will join as a hands-on founding engineer, building the foundations yourself and progressively evolving into Head of LLM Lab as the team grows. This is not a prompt engineering or LLM API integration role.
We are looking for someone who has worked directly with model weights, training pipelines, datasets and GPU infrastructure. Why this role? This is an opportunity to build an LLM capability from the ground up rather than joining an already established team.



You will work across the full model lifecycle — data → training → evaluation → optimization → GPU infrastructure → production — with significant technical ownership from day one.
What you will do
Model development & training Develop, train and fine-tune transformer-based LLMs. Adapt foundation models to verticals including enterprise, healthcare, legal, construction and hospitality. Implement supervised fine-tuning and, where relevant, preference-alignment techniques. Prepare and manage training, validation and evaluation datasets. Build reproducible evaluation pipelines and continuously monitor hallucination, overfitting, contamination and model regressions.
Inference & GPU optimization Optimize models for latency, memory, throughput and cost per token. Work with technologies such as v LLM, SGLang, Tensor RT-LLM, NVIDIA NIM, Triton and TGI. Apply techniques including quantization, distillation and spe

📌 Llm engineer (Madrid)
🏢 Substrate AI
📍 Madrid