Staff Machine Learning Engineer (Video Insights)

2 hours ago

Bengaluru, IndiaStaff+

Responsibilities

Fine-tune large video-language models using LoRA, QLoRA, PEFT, and related techniques for video understanding tasks.
Design efficient model adaptation pipelines for domain-specific video content.
Optimize model inference through quantization, knowledge distillation, and hardware-specific techniques.
Conduct experimentation and ablation studies to identify effective model configurations and hyperparameters.
Build evaluation frameworks and metrics for model quality, generalization, and edge-case performance.
Collaborate with research and product teams to translate business requirements into model-tuning objectives.
Document tuning methodologies, lessons learned, and team best practices.
Contribute to open-source projects and stay current with multimodal AI and video understanding advances.
Serve as a functional lead by providing technical guidance and expertise to team members.

6+ years of professional machine learning engineering experience focused on deep learning and model fine-tuning.
Advanced Python proficiency and hands-on experience with deep learning frameworks, preferably PyTorch.
Experience fine-tuning large language models and multimodal models using PEFT, LoRA, or similar techniques.
Strong understanding of video codecs, video processing pipelines, and streaming technologies.
Foundational knowledge of computer vision, deep learning, CNNs, Transformers, and attention mechanisms.
Experience with model evaluation frameworks, A/B testing, and continuous experimentation infrastructure.
Experience with GPU-based training and inference optimization using CUDA or similar frameworks.
Ability to debug complex machine learning systems in production.
Experience with Git and MLOps tools such as MLflow or Weights & Biases.
BE/B.Tech in Computer Science, Electrical Engineering, AI, or a related technical field; MS or PhD in ML/AI is a plus.
The preferred experience range is 7 to 9 years for candidates with the stated technical education.

501-1,000 employees

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