Principal Machine Learning Scientist, Video Intelligence

Job Description: As a Staff Machine Learning Engineer, you will demonstrate strong independence and technical proficiency while collaborating effectively within the team. You will uphold a standard of excellence, ensuring high-quality work and timely delivery of projects. Additionally, you will serve as the functional lead within your domain, providing guidance and expertise to team members. Key responsibilities: - Fine-tune large video models (Vid-LLMs) using advanced techniques such as LoRA, QLoRA, and PEFT for specific video understanding tasks - Design and implement efficient model adaptation pipelines for domain-specific video content and use cases - Optimize model inference performance through quantization, knowledge distillation, and hardware-specific optimizations - Conduct extensive experimentation and ablation studies to identify optimal model configurations and hyperparameters - Build robust evaluation frameworks and metrics to assess model quality, generalization, and edge case performance - Collaborate with research and product teams to translate business requirements into model tuning objectives - Develop and maintain documentation of tuning methodologies, lessons learned, and best practices for the team - Contribute to open-source projects and stay current with the latest advancements in multimodal AI and video understanding Skills and attributes for success: - 7+ years of professional experience in machine learning engineering, with specific focus on deep learning and model fine-tuning - Advanced proficiency in Python and hands-on experience with deep learning frameworks (PyTorch preferred) - Hands-on experience fine-tuning large language models and multimodal models using PEFT, LoRA, and similar techniques - Strong understanding of video codecs, video processing pipelines, and streaming technologies - Solid foundation in computer vision and deep learning fundamentals (CNNs, Transformers, attention mechanisms) - Experience with model evaluation frameworks, A/B testing, and continuous experimentation infrastructure - Proficiency with GPU-based training and inference optimization using CUDA or similar frameworks - Excellent problem-solving skills and ability to debug complex ML systems in production - Experience with version control (Git) and MLOps tools (MLflow, Weights & Biases, or similar) Qualification Required: - BE/B.Tech in Computer Science, Electrical Engineering, AI, or a related technical field with 9 to 12 years of experience - MS or PhD in ML/AI a plus Job Description: As a Staff Machine Learning Engineer, you will demonstrate strong independence and technical proficiency while collaborating effectively within the team. You will uphold a standard of excellence, ensuring high-quality work and timely delivery of projects. Additionally, you will serve as the functional lead within your domain, providing guidance and expertise to team members. Key responsibilities: - Fine-tune large video models (Vid-LLMs) using advanced techniques such as LoRA, QLoRA, and PEFT for specific video understanding tasks - Design and implement efficient model adaptation pipelines for domain-specific video content and use cases - Optimize model inference performance through quantization, knowledge distillation, and hardware-specific optimizations - Conduct extensive experimentation and ablation studies to identify optimal model configurations and hyperparameters - Build robust evaluation frameworks and metrics to assess model quality, generalization, and edge case performance - Collaborate with research and product teams to translate business requirements into model tuning objectives - Develop and maintain documentation of tuning methodologies, lessons learned, and best practices for the team - Contribute to open-source projects and stay current with the latest advancements in multimodal AI and video understanding Skills and attributes for success: - 7+ years of professional experience in machine learning engineering, with specific focus on deep learning and model fine-tuning - Advanced proficiency in Python and hands-on experience with deep learning frameworks (PyTorch preferred) - Hands-on experience fine-tuning large language models and multimodal models using PEFT, LoRA, and similar techniques - Strong understanding of video codecs, video processing pipelines, and streaming technologies - Solid foundation in computer vision and deep learning fundamentals (CNNs, Transformers, attention mechanisms) - Experience with model evaluation frameworks, A/B testing, and continuous experimentation infrastructure - Proficiency with GPU-based training and inference optimization using CUDA or similar frameworks - Excellent problem-solving skills and ability to debug complex ML systems in production - Experience with version control (Git) and MLOps tools (MLflow, Weights & Biases, or similar) Qualification Required: - BE/B.Tech in Computer Science, Electrical Engineering, AI, or a related technical field with 9 to