Senior Machine Learning Engineer
On-siteMunich, Bavaria, Germany
Job Summary
Own face machine learning projects end-to-end, from problem definition and data preparation through experimentation, evaluation, and production validation. Improve core biometric identification and anti-spoofing models by training deep learning architectures while adhering to strict latency, memory, and size budgets. Lead independent applied ML initiatives by forming hypotheses, designing ablations, and running experiments to isolate variables and drive model changes. Build evaluation pipelines to catch regressions, monitor data drift and new attack patterns, and improve internal tools for analysis and red teaming. Use classical computer vision and image processing as preprocessing stages or lightweight on-device components where they offer efficiency. Write design documents, experiment reports, and technical proposals that remain useful for future team members. Shape technical standards across the AI & Biometrics team regarding evaluation methodology, experimentation discipline, and model versioning.
Required Qualifications
- Significant hands-on experience training, evaluating, and shipping deep learning systems for computer vision, with practical understanding of latency and memory constraints
- Experience taking an ambiguous machine learning problem and turning it into a structured technical plan
- Strong practical knowledge of model training, including data pipelines, augmentations, architecture selection, loss functions, optimization, hyperparameter tuning, and failure analysis
- Strong foundations in classical computer vision and image processing, with experience including tools such as OpenCV, NumPy, or equivalent libraries
- Fluency in Python and a modern deep-learning framework such as PyTorch
- Experience designing evaluations that support real production decisions, including metric selection, operating thresholds, calibration, dataset construction, slicing, leakage prevention, and regression analysis
- The ability to write maintainable research and production-quality code
- Strong written communication
- A collaborative operating style: you work independently without becoming isolated, and you actively share context and knowledge with others, engaging constructively with constraints from neighboring teams rather than treating them as obstacles
- An 'in-the-driver's-seat' operating style: you take ownership of problems end-to-end, drive your work forward without waiting for direction, and stand behind your decisions once they are in production
Desired Qualifications
- Direct experience with biometric verification/identification; margin-based metric learning losses and their failure modes; presentation attack and liveness detection; or adversarial evaluation of ML systems
- Experience with edge optimization and on-device deployment of ML models. Quantization, pruning, distillation, kernel-level optimization, deployment to mobile NPUs, embedded GPUs, microcontrollers, or other constrained targets
- Hands-on experience with Rust for high-performance code paths, and the disposition to optimize for speed rather than treat it as someone else's problem
- A background in sensors, imaging, computational photography, or camera ISPs
- Familiarity with privacy-preserving computation or machine learning systems that interact with secure multi-party computation
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