Tech Writers (Research Background)

At Aye , we’re redefining how people pay, earn, and connect — using face as the universal identifier for payments, loyalty, and personalized experiences. We’re seeking tech writers with a research or academic background — individuals familiar with advanced computing and AI domains such as neural and transformer architectures, advanced learning paradigms, multimodal learning, data tokenization, blockchain systems, quantum AI, human–AI interaction, and biometric authentication. What You’ll Do You’ll join our weekly product and R&D call (1 hour) to stay close to the technology behind AyeFace, then translate those insights into educational and engaging content — newsletters, Medium articles, and X (Twitter) threads — that help audiences understand the science, security, and innovation powering AyeFace. This opportunity is ideal for PhD candidates, research assistants, or tech researchers who want to bridge deep tech with public communication and thought leadership in a fast-evolving startup environment. What We’re Looking For Background or strong interest in at least one of the following: AI Model Architectures (e.g., CNNs, Transformers, multimodal learning) Biometrics & Computer Vision Data Tokenization & Privacy-Preserving Systems Blockchain and Decentralized Identity Human–AI Interaction and User-Centric Design Quantum or Neuromorphic Computing Excellent technical communication and writing skills — able to explain complex systems in a concise, engaging way. Curious, detail-oriented, and proactive in translating technical depth into accessible narratives. Familiarity with platforms like Medium, X (Twitter), Substack, Discord or Reddit for tech thought leadership. Comfortable collaborating remotely with a startup team and managing short, creative writing cycles. How to Apply Send us an email to View email address on click.appcast.io with the subject line: “Application – Tech Writer (Research Background)” Please include: A short note (max 1 page) on a technical or research topic (e.g., model architecture, data tokenization, or privacy-preserving learning) that you think the public should better understand — and how you’d explain it. Links to any past writing or content samples (articles, threads, academic explainers, or visual explainers) that showcase your communication style. #J-18808-Ljbffr