AI Engineer
Hybrid · Barcelona, Catalonia, Spain
Job Summary
AI Engineer at Labhouse (Barcelona-based startup) responsible for owning the development, fine-tuning, and productionization of ML models powering a suite of AI & Productivity Apps. You will research, design, and train models tailored to product needs, fine-tune open-source/foundation models, manage the full model lifecycle (data curation, experimentation, evaluation, deployment), and collaborate with mobile and backend engineers to integrate models into apps. You will define and track quality metrics to push performance, and thrive in a hands-on engineering role that emphasizes autonomous decision-making, strong Python skills, and experience with ML frameworks (PyTorch or JAX). The role values a strong academic foundation (CS/EE/Math/Physics or related field) with a Master's/PhD being a strong plus, and expects demonstrated ability to train, fine-tune, or ship models in professional, research, or personal projects. Experience with the Hugging Face ecosystem, GitHub/OSS profiles, and the full model development cycle are highly advantageous. The position offers hybrid work based in Barcelona and opportunities to work directly with founders and a small, fast-moving team.
Required Qualifications
- Degree in Computer Science, Electrical Engineering, Mathematics, Physics, or related technical field
- Master's or PhD in machine learning or related area is a strong plus but not required
- Hands-on experience training or fine-tuning deep learning models used in production
- Strong Python skills
- Experience with ML frameworks (PyTorch or JAX preferred)
- Experience with full model development cycle: data pipelines, experiment tracking, evaluation, deployment and scaling
- Solid understanding of modern model architectures and when to apply them
- Experience with the Hugging Face ecosystem (Transformers, Datasets, Hub) is a strong plus
- Ability to work autonomously and ship without waiting for perfect conditions
- Nice-to-have: experience across multiple model domains (vision, audio, NLP, multimodal); on-device/edge inference optimization; integrating ML models into mobile/production environments
- Published research or open-source contributions in ML
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