Senior Machine Learning Engineer

Digital Waffle; España; Presencial; EUR 90.000 - 120.000; Jornada completa

Descripción de la vacante

Digital Waffle is building scalable ML infrastructure that bridges research and production. The role focuses on owning end-to-end ML systems—from raw data to deployed models—ensuring reliability and performance across backend, mobile, and desktop integrations.

You will work on pipelines, model fine-tuning, and inference systems with a strong emphasis on latency, cost, and quality data for training. This is a hands-on ownership role with real impact.

Formación

• Proven ability to train, fine-tune, or ship large-scale models in production.
• Deep understanding of DL and transformer architectures.
• Experience with at least one major ML framework (PyTorch, JAX).
• Familiarity with distributed training and inference tooling (DeepSpeed, FSDP, Megatron, ZeRO, Ray).

Responsabilidades

• Build end-to-end pipelines across data, training, evaluation, and inference.
• Adapt and fine-tune models with modern techniques (LoRA, QLoRA, SFT, DPO, distillation).
• Architect inference systems under latency and cost constraints.
• Create data pipelines for synthetic and real-world training data.
• Run evaluations focusing on robustness, safety, and production behavior.
• Own deployment: GPU optimization, quantisation, memory efficiency, scaling.

Conocimientos

Deep learning

Transformer architectures

PyTorch

JAX

Distributed training

GPU optimisation

Ambiguity ownership

Herramientas

DeepSpeed

FSDP

Megatron

ZeRO

Ray

TensorRT

Descripción del empleo

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.