AI Team Lead

A global, pioneering AI-tech enterprise that engineers advanced detection, verification, and digital content protection technologies (spanning text, images, audio, and video).

Operating at the forefront of digital trust, the company's platform safeguards intellectual property and content authenticity in the era of generative AI.

By building state-of-the-art multimodal classification and synthesis-detection systems, the organization enables global enterprises, media outlets, and publishers to combat misinformation and secure their digital assets.

The corporate R&D facility is based in Ramat Gan (highly accessible, situated directly along major transit networks), operating under a hybrid model providing 2 days of remote work per week.

Position Overview-

• Hands-on AI Team Leader taking direct management and technical leadership over a high-caliber squad of 4 Data Scientists and AI Engineers.
• Owning the company's core AI and Machine Learning roadmap end-to-end—driving novel deep learning architectures from early-stage conceptual research to high-scale production deployment.
• Leading the development and optimization of complex, multimodal models designed to detect synthetic content, identify manipulation, and verify digital assets.
• Designing and maintaining highly scalable, distributed ML training pipelines and robust cloud infrastructure.
• Working closely with Product and Core R&D groups to translate emerging generative AI research into highly reliable enterprise product features.
• Core Domain & Ecosystem- Multimodal AI & Deepfake Detection, Hands-on Technical Leadership, Deep Learning (PyTorch), NLP & Computer Vision, Generative AI & LLM Fine-Tuning (Transformers, PEFT), Distributed ML Training, Cloud-Native Engineering (GCP, Kubernetes).

Requirements-

• Academic Background: B.Sc. in Computer Science, Mathematics, or a highly quantitative scientific discipline – Mandatory (M.Sc. or Ph.D. in a relevant machine learning domain – Strong Advantage)
• 1 years of direct managerial experience leading teams of Data Scientists, ML Researchers, or AI Engineers – Mandatory
• 4 years of hands-on professional experience operating as a Data Scientist or AI/ML Engineer, with a proven track record of training and deploying large-scale models into production – Mandatory
• Expert-level programming and architectural mastery of Python, PyTorch, and standard scientific libraries (NumPy, Scikit-Learn) – Mandatory
• Verifiable hands-on engineering experience in at least two of the following domains: Natural Language Processing (NLP), Computer Vision (CV), Audio Processing, or Video Analysis – Mandatory
• Practical experience building, optimizing, and maintaining distributed machine learning training pipelines – Mandatory
• Familiarity with modern Large Language Model (LLM) frameworks and optimization techniques (e.g., Hugging Face Transformers, PEFT, TRL, LangChain) – Mandatory
• Hands-on engineering experience operating within Cloud-Native environments (preferably GCP), utilizing Docker and Kubernetes for model orchestration – Mandatory