ML Engineer: Build Production Models & NLP Systems

Impress; Barcelona; Presencial; EUR 70.000 - 100.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive salary

Teeth aligner and whitening benefits

International team

Fresh fruits and snacks

Descripción de la vacante

Impress is seeking a Machine Learning Engineer with 3+ years of hands-on experience to design and ship models that drive decisions across our health‑tech platforms, including scoring, ranking, forecasting, recommendations, and NLP.

You will own the full lifecycle from data extraction to deployment and monitoring, build models from gradient boosting to deep learning, apply NLP to unstructured data, and mentor others in solid ML practice within a collaborative, start‑up‑like team.

Formación

• Strong ML fundamentals: statistics, optimization, model evaluation.
• Experience with tree ensembles, linear models, deep learning.
• NLP/LLM experience: embeddings, transformers, fine-tuning or prompting.
• Production experience shipping models with monitoring and metrics.
• Solid software practices: clean code, version control, testing.

Responsabilidades

• Frame and solve diverse ML problems across scoring, ranking, forecasting, and NLP.
• Build models from gradient boosting to deep learning; select appropriate tools.
• Apply NLP to unstructured data: classification, embeddings, summarization.
• Design offline/online experiments and measure causal impact.
• Own full lifecycle: data extraction, feature engineering, training, deployment, drift monitoring.

Conocimientos

ML fundamentals

Modeling breadth

Experimentation

NLP/LLM experience

Python ecosystem

Production experience

Software fundamentals

Herramientas

PyTorch

TensorFlow

scikit-learn

NumPy

pandas

SQL

Airflow

FastAPI

Docker

Descripción del empleo

Impress is seeking a Machine Learning Engineer with 3+ years of hands-on experience to design and ship models that drive decisions across our health‑tech platforms, including scoring, ranking, forecasting, recommendations, and NLP.

You will own the full lifecycle from data extraction to deployment and monitoring, build models from gradient boosting to deep learning, apply NLP to unstructured data, and mentor others in solid ML practice within a collaborative, start‑up‑like team.