Senior Machine Learning Engineer

Digital Waffle; España; Sur place; EUR 90 000 - 150 000; Plein temps

Résumé du poste

Digital Waffle in Spain is seeking a Senior ML Engineer to own the ML stack from data to deployed models. You will bridge research and production, building end-to-end pipelines across data, training, evaluation, and inference, and architect inference systems that meet latency and cost constraints.

You will work with application engineers to integrate ML into backend, mobile, and desktop, optimise GPUs, and push robust, safe models through production with a focus on reliability and scalability.

Qualifications

• Deep understanding of deep learning and transformer architectures.
• Proven experience training, fine-tuning, or shipping large-scale models in production.
• Strong with PyTorch or JAX and quick to pick up others.
• Familiar with distributed training and inference tooling: DeepSpeed, FSDP, Megatron, ZeRO, Ray.

Responsabilités

• Build end-to-end pipelines across data, training, evaluation, and inference.
• Architect inference systems with latency and cost constraints.
• Create data pipelines for high-quality synthetic and real training data.
• Run evaluation beyond benchmarks for robustness, safety, and production behaviour.
• Own deployment: GPU optimisation, quantisation, memory efficiency, scaling.
• Collaborate with application engineers to integrate ML into backend, mobile, and desktop.

Connaissances

Deep learning

Transformers

Model training

Deployment

PyTorch

JAX

Distributed training

GPU optimisation

Code robustness

Outils

DeepSpeed

FSDP

Megatron

ZeRO

Ray

Description du poste

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.

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. 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.