Senior Machine Learning Engineer (m/f/d)

• Own face machine learning projects from initial problem definition through data preparation, experimentation, evaluation, production validation, and monitoring, while partnering with different teams.
• Improve our core biometric identification and anti-spoofing models, training and iterating on deep learning architectures, losses, and data pipelines, with model size, latency, and memory budgets as first-class design constraints from day one.
• Use classical computer vision and image processing when they are the right solution, whether as a complete method, a preprocessing stage, a lightweight on-device component, or a diagnostic tool. We are not a deep learning only team, and on-device compute rewards the discipline of using the lighter solution when it fits.
• Lead independent applied ML initiatives end-to-end: form a written hypothesis, design ablations that isolate variables, run experiments, read results honestly, and know when to ship and when to stop chasing the last percentage point
• Work directly with face images and datasets: improve collection and labels, inspect difficult samples, identify failure modes, and use those findings to drive model and evaluation changes.
• Build evaluation pipelines that catch model regressions before they reach production, monitoring that can reveal data drift, score-distribution changes, new attack patterns, cohort-specific regressions, or unexpected model behavior, and improve internal tools for data analysis, model training, evaluation, visualization, red teaming, and monitoring.
• Read and evaluate relevant computer vision and biometrics research, adapting useful ideas to our systems without treating paper reproduction as the objective.
• Write design documents, experiment reports, post-launch analysis and technical proposals that remain useful after the project is complete, and others can understand and build on your work.
• Help shape technical standards across the AI & Biometrics team: evaluation methodology, experimentation discipline, model versioning, monitoring, and across any engineering and scientific standards of the team.

• 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.
• A pragmatic, applied-research mindset: rigorous about experiments, but able to determine when a result is ready to ship and when further optimization has diminishing value.
• Strong written communication. You define the problem, requirements, assumptions, experiments, success criteria, and conclusions clearly.
• 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. We are not looking for a lone wolf, however brilliant.
• 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.

Additional Nice-to-haves :

• 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.