Cliply Seeks Senior AI/ML Engineer for Multimodal Video Intelligence
Cliply, a company focused on video understanding and content analysis for creators and enterprises, is seeking a Senior AI/Machine Learning Engineer specializing in multimodal and video intelligence. The role involves designing deep learning architectures for video, audio, and text understanding, as well as optimizing models for production-ready ML systems. This signifies development in the application of AI within streaming media workflows.
Key Takeaways
- The position requires expertise in designing deep learning architectures for temporal modeling and multimodal fusion across video, audio, and text.
- Responsibilities include optimizing models using techniques like quantization, pruning, and batching for latency, throughput, and GPU efficiency.
- The engineer will convert AI prototypes into robust, production-ready micro-services and collaborate on architectural decisions.
- Required qualifications include 5-10+ years of ML engineering experience and proficiency in PyTorch or TensorFlow, with a preference for Singapore-based candidates.
Why It Matters
Cliply's investment in a senior role for multimodal video intelligence signals a growing industry demand for advanced AI solutions in content analysis and understanding. This specialization reflects the increasing complexity of streaming media workflows, which require sophisticated tools to process diverse data types efficiently. Companies reliant on video content will need to track the performance and adoption of these advanced AI capabilities to understand their impact on content indexing, search, and automated highlight generation. Further development in this area will contribute to more intelligent content platforms and personalized viewer experiences.
Additional Context
The demand for multimodal AI engineers at companies like Cliply reflects a broader trend in the streaming industry towards leveraging AI for deeper video understanding. Twelve Labs, for example, recently showcased its Pegasus 1.5 model, which aims to transform video content into time-based metadata, allowing for natural language searches across lengthy video archives (Twelve Labs, December 2025). Similarly, Cliplyst launched a public beta of its platform, enabling users to extract key points, summaries, and transcripts from video and web content using AI (Cliplyst, February 2026). These platforms highlight the shift from basic metadata tagging to AI-driven analysis of visual, auditory, and textual elements within video. This evolution is critical for content creators and enterprises managing vast video libraries, as it enhances content discoverability, compliance, and monetization opportunities. The recruitment for specialized roles such as Cliply's Senior AI/ML Engineer indicates that sophisticated multimodal models are moving from research into practical, scalable applications within the streaming ecosystem, aiming to process and analyze video at speeds significantly faster than real-time (Twelve Labs, December 2025).
Read full article at bebee.com
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