ppJoin Impress – Europe’s Leading Health-Tech Innovator! /p pWe're looking for a strong bMachine Learning Engineer /b with 3+ years of hands‑on experience and deep fundamentals in ML algorithms and modeling. You'll design and ship models that drive decisions across our business — scoring, ranking, uplift, forecasting, recommendation, and NLP — owning each problem from formulation through production and measured impact. Our Data ML team builds the models that power patient and operations decisions at Impress, Europe's largest orthodontic clinic chain, mining signal from patient communications. You'll have room to take these further and to open up new modeling directions as the business grows. /p h3Why we're cool: /h3 ul liWork with an international and multicultural team /li liCompetitive salary /li liTeeth aligner and whitening benefits /li liCollaborative work environment and positive culture /li liOpportunities to grow within a fast‑paced, innovative company and real start‑up experience with big challenges /li liFresh fruits and healthy snacks at the office /li /ul h3What You'll Do: /h3 ul libFrame and solve diverse ML problems /b — classification, regression, ranking, uplift / causal modeling, forecasting, recommendation, anomaly detection, and some NLP processing. /li libBuild models across the algorithmic spectrum /b — from gradient boosting and classical ML to deep learning (mostly inference) — choosing the right tool, not the trendy one. /li libApply NLP / DL to unstructured data /b (text, conversations, communications): classification, intent detection, embeddings, summarization, information extraction. /li libDesign experiments and A/B tests /b — define offline metrics and online success criteria, reason about baselines, causal effects, and statistical significance, and prove that models actually move the needle.



/li libOwn the full lifecycle /b — data extraction and feature engineering, training and evaluation, deployment, retraining, and monitoring for drift and data quality. /li libSet the technical bar /b — bring rigor to evaluation, guard against leakage and overfitting, and mentor on solid ML practice. /li /ul h3Requirements: /h3 ul libStrong ML fundamentals /b: probability and statistics, optimization, bias/variance, regularization, model evaluation, and a real understanding of the algorithms behind the libraries. /li libBreadth of modeling experience /b: tree ensembles (boosting/bagging), linear models, clustering, and deep learning (CNNs/RNNs/transformers) — and the judgment to choose between them. /li libExperimentation /b: Familiarity with uplift / causal inference and experimentation, or a strong drive to master it. /li libNLP / LLM experience /b (embeddings, transformers, fine‑tuning or prompting). /li libTechnical Stack /b: Strong Python and the ML ecosystem (NumPy, pandas, scikit‑learn; PyTorch or TensorFlow; gradient‑boosting libraries). /li libProduction Track Record /b: shipping models to production, not just notebooks — and measuring their impact. /li libSoftware Fundamentals /b: clean code, SQL, version control, testing. /li /ul h3Nice to have: /h3 ul libMLOps maturity /b: experiment tracking, CI/CD for models, feature stores, model monitoring. /li libInfrastructure /b: Cloud (AWS / GCP), data warehouses, orchestration (Airflow or similar), serving (FastAPI, Docker). /li libCommunity /b: Publications, competitions. /li /ul pAt Impress we cultivate a culture of inclusion and diversity. We celebrate our employees' individual strengths, views, and experiences and we encourage all candidates to apply, without regard to race, color, religion, gender identity, sexual orientation, age, national origin, disability, or any other f actor. /p /p #J-18808-Ljbffr

📌 Machine Learning Engineer (Barcelona)
🏢 Impress
📍 Barcelona