Senior Machine Learning Engineer

About the role

We are seeking a Senior Machine Learning Engineer in the Hyderabad area to design, build, and scale machine learning systems that power personalized and data-driven experiences across our digital entertainment ecosystem — including paid VOD streaming, Manga reading, Merchandising, Mobile Gaming, and Music Video platforms. The role will be reporting to the Manager of Machine Learning.

You will work at the intersection of user behavior, content intelligence, and product engagement, building models and systems that both enhance user experience (e.g., personalization, discovery, lifecycle optimization) and generate actionable business insights and services for internal stakeholders. As a senior individual contributor, you will own high-impact initiatives end-to-end and play a key role in shaping the technical direction of ML systems across products.

Core Areas of Responsibility

• Design, develop, and deploy scalable machine learning models that enhance personalization, discovery, engagement, and monetization across multiple product verticals.
• Lead end-to-end ML projects from problem framing and data exploration to production deployment and monitoring.
• Build and optimize robust data pipelines and feature engineering workflows for large-scale behavioral and content data.
• Design and execute rigorous experimentation frameworks (e.g., A/B testing, offline evaluation) to measure model impact.
• Collaborate closely with Product, Engineering, Analytics, and business stakeholders to translate product goals into ML solutions.
• Improve system reliability, scalability, and performance of production ML services.
• Mentor junior engineers and contribute to raising the team’s technical standards and best practices.
• Contribute to the evolution of ML architecture, tooling, and MLOps practices across the organization.

About You

Required Qualifications

• 8+ years of experience building and deploying machine learning systems in production environments
• Strong foundation in machine learning algorithms, statistics, and model evaluation techniques
• Experience working with large-scale user behavior or content datasets
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, xgboost)
• Experience with distributed data processing and data pipeline technologies
• Strong understanding of experimentation methodologies and performance measurement
• Ability to operate independently and drive ambiguous problems to impactful solutions

Preferred Qualifications

• Experience with recommendation systems, ranking models, personalization, or user lifecycle modeling
• Experience in subscription-based digital products, streaming media, gaming, or e-commerce platforms
• Familiarity with cloud infrastructure and production ML services
• Experience optimizing models for real-time or near real-time user-facing applications
  
  

What Success Looks Like

• ML systems you build measurably improve user engagement, retention, or monetization
• Models are reliable, scalable, and well-integrated into product workflows
• Stakeholders trust your technical judgment and rely on your work to inform product direction
• You elevate the team’s engineering rigor and contribute meaningfully to long-term ML platform evolution

About the Team

You will join a fast growing team of Data Scientists, Machine Learning Engineers, and AI Engineers, united by a passion for leveraging data and AI to create transformative solutions. Our team delivers both consumer-facing product features that enhance the streaming experience for millions of users and stakeholder-oriented solutions that empower internal teams with actionable insights. From building personalized recommendation systems to developing innovative tools like our stakeholder-facing chatbot, we combine cutting-edge machine learning, robust data pipelines, and advanced AI to drive impact across the organization. Collaboration, creativity, and a commitment to excellence define our team’s culture as we work together to push the boundaries of what’s possible in streaming and data-driven decision-making.