Senior Machine Learning Engineer

Tools for Humanity·Munichsenior

En corto

→Ingeniero de ML Senior que desarrolla sistemas de verificación facial y anti-spoofing a escala global.
→Trabaja en todo el ciclo de ML: datos, entrenamiento, evaluación, despliegue y monitoreo en producción.
→Destaca por integrar modelos de IA con criptografía privada y operar en condiciones reales con alta precisión y bajo costo computacional.

Fluency in English required for collaboration across global teams.

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¿Qué piden?

✓Experiencia comprobada en machine learning aplicado al reconocimiento facial.
✓Conocimiento profundo de técnicas de visión por computadora y detección de ataques de presentación.
✓Capacidad para trabajar en todo el ciclo de vida del ML: datos, entrenamiento, evaluación y despliegue.
✓Experiencia con modelos de deep learning y optimización para dispositivos móviles.
✓Comprensión de sistemas de privacidad como MPC (Anonymized Multi-Party Computation).
✓Habilidades en evaluación rigurosa de modelos y monitoreo de rendimiento en producción.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PyTorchTensorFlowOpenCVCUDADockerKubernetesMLflowAirflowGCPGit

¿A quién escribirle en Tools for Humanity?

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About the Company: Tools for Humanity (TFH) designs and builds technology behind World. World is building a real human network designed to accelerate people in the age of AI. As bots and autonomous agents reshape the internet, people, institutions, and applications need a trusted way to confirm who is a real human while preserving privacy. The TFH and World tech stacks make this possible: the Orb verifies real, unique people, World ID proves it privately, and World App puts these capabilities, and more, in people’s hands. Together, they add a human layer to an AI-driven internet. World is already running at a global scale. More than 17 million people across 160 countries have verified with World ID, and more new Orb verifications take place each week. World App is already among the most used wallets globally. Developers are integrating World ID to build safer online experiences and create spaces where real people can participate, earn, and be recognized in ways AI simply can’t replicate. Founded in 2019, TFH has more than 400 people across hardware, software, AI, cryptography, mobile engineering, and global operations. Our teams come from OpenAI, Tesla, SpaceX, Apple, Google, Stripe, Meta, Coinbase, Palantir and MIT Media Lab. We’re backed by leading investors, including a16z, Khosla Ventures, Bain Capital Crypto, Blockchain Capital, Variant, Tiger Global, and Coinbase Ventures, as well as prominent operators and founders across fintech and AI. TFH and World have been featured on the cover of TIME Magazine , highlighted in Fast Company’s Next 5 in Fintech, and explored in a Bloomberg deep dive . The New York Times , Bankless and TechCrunch have all recognized our collective progress in identity, cryptography, AI, and global-scale hardware deployment. Our leadership is also named to the Time AI 100 . Learn more about the newest product launches from our Liftoff event. About the team The AI & Biometrics team at Tools for Humanity owns the machine learning systems behind the World Network. Our iris and face recognition systems, our anti-spoofing pipeline, and the models running on the Orb and on phones are what make Proof of Personhood actually work at scale. Within the Face team, we develop systems for face verification, uniqueness and duplicate detection, presentation attack detection, and supporting capabilities such as face detection, image quality assessment, occlusion detection, pose estimation, and others that allow these systems to operate reliably in real-world conditions. Our work spans the full machine learning lifecycle: defining data-collection and labeling requirements, curating datasets, conducting applied computer-vision research, training and evaluating models, adversarial testing, supporting deployment, investigating production behavior, and monitoring performance after release. The problems we work on are grounded in real-world conditions. Our systems must handle variation in cameras, lighting, pose, image quality, occlusion, user behavior, and attack methods. They must also operate within strict latency and memory budgets and interact with privacy-preserving systems (Anonymized Multi-Party Computation) that compare users against a growing identity set. At our scale, small regressions can have a meaningful impact, so evaluation, operating-point selection, and production monitoring are central to how we work. We are pragmatic about our methods. We use deep learning where it provides clear value, classical computer vision and image processing where they are more efficient or reliable, and hybrid approaches when they produce the best system. The team is well supported. We have a dedicated Mobile subteam that owns deployment to mobile devices, ML Infrastructure and MLOps subteams that own the data pipelines, training clusters, GPU fleet, and much of the underlying tooling, and Face and Iris subteams dedicated to each modality.

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