Senior Machine Learning Engineer

About This Opportunity

We are representing a confidential technology organization building large-scale biometric and identity systems. Our talent team is seeking a Senior Machine Learning Engineer to join an AI and biometrics division working on face recognition, verification, and anti-spoofing models deployed globally on mobile devices and specialized hardware.

This role is based in Munich and offers the opportunity to work on production ML systems that operate under strict latency, memory, and privacy constraints while serving millions of users across diverse real-world conditions.

Responsibilities

• Own machine learning projects end-to-end: problem definition, data preparation, experimentation, evaluation, production validation, and monitoring
• Develop and improve biometric identification and anti-spoofing models using deep learning architectures, custom losses, and data pipelines optimized for model size, latency, and memory constraints
• Apply classical computer vision and image processing techniques when appropriate, using lighter solutions for on-device compute where they fit
• Lead independent applied ML initiatives: formulate hypotheses, design ablation studies, run experiments, and make informed decisions about when to ship
• Work directly with image datasets to improve collection methods, labels, and identify failure modes that drive model improvements
• Build evaluation pipelines to catch regressions before production, and monitoring systems to detect data drift, score-distribution changes, attack patterns, and unexpected behavior
• Review computer vision and biometrics research literature and adapt relevant ideas to production systems
• Write technical documentation including design documents, experiment reports, and post-launch analyses that enable knowledge transfer
• Contribute to technical standards for evaluation methodology, experimentation discipline, model versioning, and monitoring

What We're Looking For

Required qualifications:

• Significant hands-on experience training, evaluating, and deploying deep learning systems for computer vision with understanding of latency and memory constraints
• Proven ability to transform ambiguous ML problems into structured technical plans
• Strong practical knowledge of model training: data pipelines, augmentations, architecture selection, loss functions, optimization, hyperparameter tuning, and failure analysis
• Strong foundations in classical computer vision and image processing, with experience using OpenCV, NumPy, or equivalent libraries
• Fluency in Python and modern deep-learning frameworks such as PyTorch
• Experience designing evaluations for production decisions: metric selection, operating thresholds, calibration, dataset construction, slicing, and regression analysis
• Ability to write maintainable research and production-quality code
• Pragmatic applied-research mindset: rigorous experimentation balanced with knowing when to ship
• Strong written communication skills for documenting problems, requirements, experiments, and conclusions
• Collaborative working style with ability to work independently while sharing context and engaging constructively with cross-functional constraints
• Ownership mentality: driving work forward without waiting for direction and standing behind production decisions

Preferred experience:

• Biometric verification/identification systems
• Margin-based metric learning losses and their failure modes
• Presentation attack detection and liveness detection
• Adversarial evaluation of ML systems
• Edge optimization and on-device ML deployment: quantization, pruning, distillation, kernel-level optimization, mobile NPUs, embedded GPUs
• Rust for high-performance code paths
• Background in sensors, imaging, computational photography, or camera ISPs
• Privacy-preserving computation or ML systems interacting with secure multi-party computation

How We Work with You

Candidates apply through Get A Job.ai. Our recruiting team conducts an initial screening to understand your background and ensure alignment with the role requirements. We then submit qualified candidates to our client for their review and interview process. Please apply only through Get A Job.ai—direct contact with the client is not appropriate at this stage.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to inclusive recruiting practices. We welcome applications from candidates of all backgrounds and work to ensure fair consideration throughout our process.