As our AI Team Lead, you will sit at the intersection of cutting-edge research and production engineering, guiding a high-performing team of Data Scientists and Engineers. This is a deeply hands-on leadership role where you will drive the end-to-end lifecycle-from initial research and PyTorch training to deploying sophisticated multi-modal AI (Text, Image, Audio, and Video) in high-throughput environments. Join us in building the future of responsible AI adoption and digital integrity.
Responsibilities
Manage and grow a high-performing team of AI researchers and engineers, fostering technical excellence.
Drive the design and training of multimodal models applications (Text, Image, Audio, Video) using PyTorch.
Own the AI roadmap from initial concept to scalable, high-throughput production.
Partner with Product, Backend, and Security teams to integrate AI solutions globally.
Stay at the forefront of GenAI/LLM research to continuously improve our detection engines.
Requirements:
BSc in Computer Science, Mathematics, or a related quantitative field (MSc/PhD preferred).
4+ years of hands-on experience as a Data Scientist or AI Engineer, with a proven track record of training and deploying models at scale.
1+ years of experience leading or managing a technical team.
Expert-level proficiency in PyTorch and the usual ML stack (Python, NumPy, cikit-learn, etc.).
Demonstrated experience working with at least two of the following domains: NLP, Computer Vision, Audio processing, or Video analysis.
Experience with Cloud-native environments (GCP preferred), Docker, and Kubernetes.
Familiarity with modern LLM frameworks (Transformers, Peft, TRL, LangChain, etc.).
Experience building and maintaining distributed data pipelines for ML training.
BSc in Computer Science, Mathematics, or a related quantitative field (MSc/PhD preferred).
4+ years of hands-on experience as a Data Scientist or AI Engineer, with a proven track record of training and deploying models at scale.
1+ years of experience leading or managing a technical team.
Expert-level proficiency in PyTorch and the usual ML stack (Python, NumPy, cikit-learn, etc.).
Demonstrated experience working with at least two of the following domains: NLP, Computer Vision, Audio processing, or Video analysis.
Experience with Cloud-native environments (GCP preferred), Docker, and Kubernetes.
Familiarity with modern LLM frameworks (Transformers, Peft, TRL, LangChain, etc.).
Experience building and maintaining distributed data pipelines for ML training.
This position is open to all candidates.
