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Strong global brand but senior, niche ML/AI specialization moderates applicant density.
Advanced multimodal ML and inference expertise is transferable but requires deep domain and systems experience.
Explicit 10+ years, PhD/Master's preference and specific ML, systems, and tooling requirements increase strictness.
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Lead the technical architecture and long-term vision for AI/ML systems powering media understanding and generation at scale.
Design and oversee multimodal machine learning ecosystems integrating visual, audio, and textual data with petabyte-scale annotation and processing.
Drive innovation and optimization of inference systems and evaluation frameworks across global media catalogs, collaborating with cross-functional and executive stakeholders.
10+ years in machine learning, including at least 2 years with LLMs, diffusion models, or generative image/video models.
Expert-level skills in Python, Java, or C++ with knowledge of multi-threading, memory management, and distributed computing (Spark, Flink).
Advanced proficiency with deep learning frameworks such as PyTorch or TensorFlow, and experience in computer vision techniques (YOLO, Mask R-CNN) and NVIDIA deployment tools (DeepStream, Triton, TensorRT).
Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or related quantitative field.
Experienced in architecting and deploying large-scale ML systems with big data and MLOps tools like Kafka, Airflow, Kubernetes, and cloud AI services (AWS Bedrock, SageMaker).
Capable of bridging AI research and product strategy to lead foundational platforms rather than one-off tactical projects.
Strong influencer comfortable driving technical vision and mentoring within data science and ML engineering communities without formal authority.