Job Summary: 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.
About the team: Join our Video CoE team as a Senior ML Engineer focused on fine-tuning and optimizing large video models. 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.
Join 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!
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)
Preferred education and experience:
- BE/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
