Senior Staff Machine Learning Engineer (Video Insights)
Jiostar Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Jiostar. Bengaluru.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Fine-tune large video models for video-understanding tasks, using methods such as LoRA, QLoRA and PEFT
- Build adaptation pipelines for specialized video content, then speed up inference with quantization and hardware tuning
- Run experiments and ablation studies to choose model settings, and create measures for quality, generalization and edge cases
- Work with research and product teams to turn business needs into tuning goals, and document approaches and lessons
- Contribute to open-source work and keep up with developments in multimodal AI and video understanding
What you bring
- At least seven years in machine learning engineering, focused on deep learning and adapting models
- Strong Python skills and practical experience with deep learning frameworks, with PyTorch preferred
- You have fine-tuned large language and multimodal models using PEFT, LoRA or comparable methods
- You understand video codecs, processing workflows and streaming, alongside core computer vision and deep learning
- You have experience with model evaluation, A/B testing and experimentation systems, plus GPU optimization using CUDA or similar
Who this fits
This role suits an experienced individual contributor who can work independently and guide teammates as the functional lead for the domain. The posting describes a preferred technical degree and indicates nine to twelve years of experience for that education path; an ML/AI master's or doctorate is a plus. The role is based in Bengaluru, India.
From the employer
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:
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