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 iteration
- Design and implement ML systems that are scalable, observable, and production-ready, applying MLOps best practices
- Apply classical, neural network, and generative ML techniques appropriately, knowing when simpler models outperform more complex ones
- Research and implement state-of-the-art algorithms when needed
- Collaborate across disciplines with product managers, data engineers, backend engineers, and other stakeholders to align on goals and trade-offs
- Mentor and collaborates with colleagues through code review, design review, documentation, and hands-on technical guidance
- Participate in the full software lifecycle: requirements, design, implementation, testing, release, and operational support
- Align on priorities, timelines, and technical direction while driving execution within your scope
Required Qualifications:
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline; a Master's degree (MSc) is a plus
- Over 6 years of experience developing machine learning algorithms
Machine Learning & Research
- Deep experience training, tuning, and evaluating ML models in real-world settings
- Strong 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 optimization
- Hands-on experience with generative ML: generative models, fine-tuning, prompt/workflow design, evaluation of generative outputs, and practical deployment considerations
- Solid understanding of model evaluation: cross-validation, leakage prevention, calibration, bias/variance trade-offs, and metric selection tied to business outcomes
- Ability to read, synthesize, and apply research, and to judge when research-grade complexity is (or isn't) justified
- Experience with major ML stacks (e.g., scikit-learn, XGBoost/LightGBM, PyTorch, TensorFlow, Hugging Face) , not necessarily all of them
MLOps & Production ML
- Familiarity with MLOps workflows: experiment tracking, model versioning, reproducible pipelines, CI/CD for ML, and model registry patterns is a plus
- Familiarity with deployment and serving (batch, streaming, or API-based) and production monitoring (drift, performance degradation, data quality) is a plus
- Practical knowledge of cloud ML infrastructure (e.g., AWS, GCP, or Azure ML services, Kubernetes, or equivalent) is a plus
Software Engineering
- Proficiency in Python
- Solid fundamentals in the software development lifecycle: design, coding standards, testing, code review, versioning, and incremental delivery
- Experience building maintainable ML code, not just notebooks
Communication & Collaboration
- Excellent written and spoken English, with the ability to write clear technical docs, design notes, and stakeholder updates
- Outstanding communication skills, able to explain complex ML concepts to both technical and non-technical audiences
- Comfortable presenting results, risks, and recommendations to cross-functional partners
