Senior Staff Machine Learning Engineer (Video Insights)

You'll work with cutting-edge multimodal AI technology, experimenting with the latest video understanding models and customizing them for real-world use cases. Your work will directly impact millions of users by enabling smarter video insights and content understanding. Be part of a team that combines deep ML expertise with the infrastructure to deploy at scale.\n\nJoin our cutting-edge technology team focused on advancing video understanding through custom-tuned large video models.

If you want to tackle hard and interesting ML problems at scale and create an impact within an entrepreneurial environment, join us!\n\nKey responsibilities:\nFine-tune large video models (Vid-LLMs) using advanced techniques such as LoRA, QLoRA, and PEFT for specific video understanding tasks\nDesign and implement efficient model adaptation pipelines for domain-specific video content and use cases\nOptimize model inference performance through quantization, knowledge distillation, and hardware-specific optimizations\nConduct extensive experimentation and ablation studies to identify optimal model configurations and hyperparameters\nBuild robust evaluation frameworks and metrics to assess model quality, generalization, and edge case performance\nCollaborate with research and product teams to translate business requirements into model tuning objectives\nDevelop and maintain documentation of tuning methodologies, lessons learned, and best practices for the team\nContribute to open-source projects and stay current with the latest advancements in multimodal AI and video understanding\n\nSkills and attributes for success:\n7+ years of professional experience in machine learning engineering, with specific focus on deep learning and model fine-tuning\nAdvanced proficiency in Python and hands-on experience with deep learning frameworks (PyTorch preferred)\nHands-on experience fine-tuning large language models and multimodal models using PEFT, LoRA, and similar techniques\nStrong understanding of video codecs, video processing pipelines, and streaming technologies\nSolid foundation in computer vision and deep learning fundamentals (CNNs, Transformers, attention mechanisms)\nExperience with model evaluation frameworks, A/B testing, and continuous experimentation infrastructure\nProficiency with GPU-based training and inference optimization using CUDA or similar frameworks\nExcellent problem-solving skills and ability to debug complex ML systems in production\nExperience with version control (Git) and MLOps tools (MLflow, Weights & Biases, or similar)\n\nPreferred education and experience:\nBE/B.Tech in Computer Science, Electrical Engineering, AI, or a related technical field with 9 to 12 Yrs of experience MS or PhD in ML/AI a plus