Staff/Principal Research Engineer

About the Company:

World is a network of real humans, built on privacy-preserving proof-of-human technology, and powered by a globally inclusive financial network that enables the free flow of digital assets for all. It is built to connect, empower, and be owned by everyone.

This opportunity would be with Tools for Humanity.

About the AI & Biometrics Team:

The AI & Biometrics team is building a biometric recognition system that can work reliably with more than a billion users and enables them to claim their free share of WLD. We use cutting-edge machine learning models deployed on custom hardware to enable high-quality image acquisition, identification, and fraud prevention, all while requiring minimal user interaction.

We are building a biometric recognition and fraud detection engine that works on the 1bn people scale. Therefore, its performance needs to out-perform all the current recognition technologies. We leverage our powerful custom-made iris recognition and presentation attack detection device, the Orb, combined with the latest research from the field of AI and Deep Learning.

About the Opportunity:

As World’s user base continues to expand, ensuring the accuracy and reliability of our systems is critical. We are looking for a Staff or Principal Research Engineer to join our AI & Biometrics team. In this key role, you will develop, deploy, and enhance models for Biometric Recognition and Presentation Attack Detection. These models will power our custom hardware—the Orb—and mobile platforms like the World App.

Key Responsibilities:

• Lead the development of advanced deep learning models and classical algorithms optimized for deployment on custom hardware and mobile platforms.
• Build and maintain scalable, efficient deep learning model training pipelines.
• Oversee the entire model lifecycle, from directing internal data collection efforts to building, deploying, monitoring, and refining models.

About You:

• 5+ years of industry experience in Computer Vision, Deep Learning, and/or Data Science.
• M.Sc. or PhD in Computer Science, Data Science, Electrical Engineering, or a related STEM field.
• Proven ability to conduct research and translate findings into practical applications. Publications, patents, or contributions to the research community are a plus.
• Extensive experience training, deploying, and iterating on deep learning models with significant impact in production environments.
• Advanced programming skills in Python, with expertise in data science and computer vision libraries such as OpenCV, Pandas, NumPy, and Seaborn.
• Strong experience with deep learning frameworks, particularly PyTorch and its ecosystem.
• Familiarity with cloud-based platforms and tools (e.g., AWS, GCP, Azure) and NoSQL databases (e.g., MongoDB, Elasticsearch, Redis).
• Exceptional communication skills, including fluency in English.
• Thrive in a fast-paced environment with a proven ability to deliver high-quality results under tight deadlines.

Nice to Have:

• Expertise in training models for Deepfake detection, Presentation Attack Detection, Biometrics, or vision-based fraud detection.
• Experience deploying and maintaining models on embedded or resource-constrained hardware platforms.
• Proficiency in Rust or similar systems programming languages.
• Significant contributions to the research community in relevant fields.

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Pay transparency statement (for CA and NY based roles): 

The reasonably estimated salary for this role at TFH ranges from $250,000 - $350,000, plus a competitive long term incentive package. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition,  TFH offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, vision, 401(k) plan and match, life insurance, flexible time off, commuter benefits, professional development stipend and much more!

By submitting your application, you consent to the processing and internal sharing of your CV within the company, in compliance with the GDPR