Senior or Staff Machine Learning Engineer / Research Scientist (AI)

TL;DR

Senior or Staff Machine Learning Engineer / Research Scientist (AI): Developing and optimizing core biometric identification and anti-spoofing systems for a global human network with an accent on deep learning and classical computer vision. Focus on deploying high-performance models under tight on-device SLA and memory constraints.

Location: On-site in Munich, Germany

Company

hirify.global builds the technology behind World, creating a trusted way to confirm human identity in the age of AI.

What you will do

• Iterate on deep learning architectures and data pipelines for biometric identification and anti-spoofing.
• Implement classical computer vision and image processing solutions for preprocessing and diagnostic tooling.
• Lead independent research initiatives from written hypothesis and ablation to production deployment.
• Develop evaluation and monitoring pipelines to prevent model regressions and detect data drift.
• Collaborate with Mobile and Embedded teams to translate ML prototypes into deployed binaries.
• Mentor junior engineers and shape technical standards for experimentation and model versioning.

Requirements

• Strong fundamentals in classical computer vision using tools like OpenCV and NumPy.
• Proven experience shipping production-quality deep learning models for computer vision.
• Ability to optimize models for tight latency and memory constraints on edge or embedded devices.
• Pragmatic applied-research mindset combined with strong mathematical fluency.
• Disciplined approach to experimentation, including isolating variables and honest result analysis.
• Collaborative operating style with a commitment to knowledge sharing and mentorship.

Nice to have

• Experience with biometric identification at scale and margin-based metric learning (ArcFace, Triplet).
• Knowledge of anti-spoofing and presentation attack detection (PAD).
• Proficiency in Rust for high-performance code paths.
• Expertise in quantization, pruning, and deployment to mobile NPUs or embedded GPUs.
• Background in sensors, imaging, or computational photography.