Staff Machine Learning Engineer (Video Insights)

Jio Star; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

Jio Star is seeking a Staff Machine Learning Engineer to fine-tune large video models using cutting-edge techniques. The role involves designing efficient model adaptation pipelines and collaborating with product teams on model tuning objectives.

The ideal candidate will have at least 6 years of experience in machine learning engineering, a strong command of Python, and knowledge of deep learning frameworks like PyTorch. This position is pivotal in enhancing video understanding capabilities.

Qualifications

• 6+ years of professional experience in machine learning engineering.
• Advanced proficiency in Python and hands-on experience with deep learning frameworks.
• Experience with fine-tuning large language models.

Responsibilities

• Fine-tune large video models using advanced techniques.
• Design and implement efficient model adaptation pipelines for video content.
• Conduct extensive experimentation and abide studies.

Skills

Machine learning engineering

Deep learning

Model fine-tuning

Python

PyTorch

Computer vision

MLOps tools

Version control (Git)

Education

BE/B.Tech in Computer Science, Electrical Engineering, AI

MS or PhD in ML/AI

Tools

CUDA

MLflow

Weights & Biases

Job description

## Staff Machine Learning Engineer (Video Insights)Applylocations: Bengaluruposted on: Posted 5 Days Agojob requisition id: JR10047**Job Summary:**As a Staff Software 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:**Subscriptions and Payments Team powers the end-to-end direct monetization engine for India’s largest subscription platform, with over 250M+ subscriptions. We build scalable, multi-tenant systems for acquiring users via D2C and partner channels, reducing churn through intelligent renewal and retry mechanisms, and integrating deeply with global and local payment ecosystems like UPI Autopay and card mandates. Our platform drives seamless onboarding, high retention, and reliable payments at scale—enabling frictionless subscription experiences across geographies and devices. **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:*** 6+ 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 7 to 9 Yrs of experience MS or PhD in ML/AI a plus