Most AI products are wrappers. We're building the real thing, an agent that takes on genuine tasks for everyday users: running errands, managing workflows, holding context across long and complex conversations. Reliable by design, not by luck.

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We're small, we move fast, and the ML layer is the product. We need someone to own it.
The role
You'll bridge research and production, taking ideas and turning them into systems that run at scale, stay reliable, and get better over time. Full-stack ML ownership: from raw data to deployed model.
Day to day that looks like:
Building end-to-end pipelines across data, training, evaluation, and inference
Adapting and fine-tuning models with modern techniques: LoRA, QLoRA, SFT, DPO, distillation
Architecting inference systems that hold up under real latency and cost constraints
Creating data pipelines that produce high-quality synthetic and real-world training data
Running evaluation that goes beyond benchmarks: robustness, safety, bias, production behaviour
Owning deployment: GPU optimisation, quantisation, memory efficiency, scaling
Working directly with application engineers so ML integrates cleanly into backend, mobile, and desktop




Your skills and experience
Deep understanding of deep learning and transformer architectures
Proven experience training, fine-tuning, or shipping large-scale models in production
Strong with at least one major ML framework (PyTorch, JAX) and quick to pick up others
Familiar with distributed training and inference tooling: DeepSpeed, FSDP, Megatron, ZeRO, Ray
Engineering discipline: code that's readable, robust, and maintainable
Experience optimising for GPU constraints: quantisation, mixed precision, memory
Comfortable taking ownership of ambiguous problems from zero to one
Ships, iterates, learns from production
Nice to have
LLM inference frameworks: vLLM, TensorRT-LLM, FasterTransformer
RLHF: PPO, DPO, ORPO
Open-source contributions to ML or systems libraries
Scientific computing, compiler, or GPU kernel experience
At a big company, ML work gets absorbed into a machine. Here, your systems are the product. xqbhyrx You'll work closely with research and engineering leadership, have real influence over how the architecture evolves, and see the direct impact of your work on users. If you want to build ML infrastructure that actually matters, this is it.
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📌 Senior Machine Learning Engineer (Madrid)
🏢 Digital Waffle
📍 Madrid