Principal Machine Learning Engineer

We are looking for a dedicated Principal Machine Learning Engineer to join our growing Core team. This team collaborates closely with one of the prestigious automotive companies based in Germany, working at the forefront of innovation in AI-driven systems.Responsibilities:Own major parts of the ML lifecycle, from problem framing and experimentation through training, evaluation, deployment, monitoring, and iterationDesign and implement ML systems that are scalable, observable, and production-ready, applying MLOps best practicesApply classical, neural network, and generative ML techniques appropriately, knowing when simpler models outperform more complex onesResearch and implement state-of-the-art algorithms when neededCollaborate across disciplines with product managers, data engineers, backend engineers, and other stakeholders to align on goals and trade-offsMentor and collaborates with colleagues through code review, design review, documentation, and hands-on technical guidanceParticipate in the full software lifecycle: requirements, design, implementation, testing, release, and operational supportAlign on priorities, timelines, and technical direction while driving execution within your scopeRequired Qualifications:Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline; a Master's degree (MSc) is a plusOver 6 years of experience developing machine learning algorithms Machine Learning & ResearchDeep experience training, tuning, and evaluating ML models in real-world settingsStrong command of classical ML algorithms (e.g., linear/logistic regression, tree-based methods, clustering, dimensionality reduction, time-series basics)Experience with neural networks: architectures (MLPs, CNNs, RNNs/transformers where relevant), training dynamics, regularization, and optimizationHands-on experience with generative ML: generative models, fine-tuning, prompt/workflow design, evaluation of generative outputs, and practical deployment considerationsSolid understanding of model evaluation: cross-validation, leakage prevention, calibration, bias/variance trade-offs, and metric selection tied to business outcomesAbility to read, synthesize, and apply research, and to judge when research-grade complexity is (or isn't) justifiedExperience with major ML stacks (e.g., scikit-learn, XGBoost/LightGBM, PyTorch, TensorFlow, Hugging Face) , not necessarily all of themMLOps & Production MLFamiliarity with MLOps workflows: experiment tracking, model versioning, reproducible pipelines, CI/CD for ML, and model registry patterns is a plusFamiliarity with deployment and serving (batch, streaming, or API-based) and production monitoring (drift, performance degradation, data quality) is a plusPractical knowledge of cloud ML infrastructure (e.g., AWS, GCP, or Azure ML services, Kubernetes, or equivalent) is a plusSoftware EngineeringProficiency in PythonSolid fundamentals in the software development lifecycle: design, coding standards, testing, code review, versioning, and incremental deliveryExperience building maintainable ML code, not just notebooksCommunication & CollaborationExcellent written and spoken English, with the ability to write clear technical docs, design notes, and stakeholder updatesOutstanding communication skills, able to explain complex ML concepts to both technical and non-technical audiencesComfortable presenting results, risks, and recommendations to cross-functional partners