Machine Learning Engineer II
Indexed description
Responsibilities
- Recommendation Systems: Design and develop large-scale recommendation systems serving millions of users, with a strong focus on personalization, scalability, and efficiency.
- ML Execution: Drive the ML execution, specifically around feed ranking and recall-oriented candidate generation systems.
- Agentic Systems & GenAI: Design and implement advanced agentic frameworks (e.g., using LangGraph) utilizing LLMs and VLMs for end-to-end content lifecycle management, including automated metadata enrichment, deep content understanding, and novel interactive recommendation loops.
- Monetization Prediction: Develop and deploy predictive ML models for user monetization events, including subscription likelihood, trial conversion, and virtual currency (coins) usage forecasting.
- Marketing Intelligence & Optimization: Design and implement machine learning solutions to optimize marketing spend and campaign performance, leveraging data from platforms like Meta.
- Technical Ownership: Provide guidance in ML model formulation, experimentation, and deployment. Take end-to-end ownership of ML systems, including key user-satisfaction metrics.
- Architecture & Strategy: Contribute to the architectural strategy of complex ML systems, integrating agentic reasoning to improve how systems meet the needs of users and content stakeholders.
- MLOps & Infrastructure: Design and maintain robust, low-latency deployment and monitoring pipelines for large-scale recommendation and GenAI models, ensuring high reliability and performance.
- Model Training & Serving: Hands-on experience training and serving large-scale ML models using frameworks such as PyTorch or TensorFlow.
- Production Experience: Proven track record of productionising machine learning models, including designing and managing end-to-end ML systems and data pipelines, leveraging technologies like Feature Stores, specialized vector databases (e.g., Pinecone, Milvus), or cloud-native MLOps platforms.
- Recommendation & GenAI Expertise: Direct experience in building large-scale (million+ users) solutions for feed ranking. Familiarity with LLM orchestration frameworks (e.g., LangChain, LangGraph) is highly desirable.
- Monetization & LTV Experience: Practical experience developing ML models for predicting user lifetime value (LTV), churn, or subscription conversion.
- Marketing Analytics Focus: Experience in marketing analytics, attribution modeling, and processing data from advertising platforms like Meta to inform business strategy.
- Research Awareness: Stay up-to-date with the latest advancements in recommender systems, Generative AI agents, and applied machine learning.
- Education & Experience: Bachelor’s or Master’s in Computer Science, Machine Learning, Statistics, or a related engineering field, with 3+ years of relevant experience or 2+ years combined with a Master’s degree, with a proven ability to own and deliver complex ML systems.
- Opportunity to work in a fast-growing audio and content platform.
- Exposure to multi-language marketing and global user base strategies.
- A collaborative work environment with a data-driven and innovative approach.
- Competitive salary and growth opportunities in marketing and growth strategy.
We deliver immersive entertainment and education through our OTT platforms: Kuku FM, Guru, Kuku TV, and more. With a mission to provide high-quality, personalized stories across genres in entertainment, spanning multiple formats and languages, KUKU continues to push boundaries and redefine India’s entertainment industry.
🌐 Website: www.kukufm.com
📱 Android App: Google Play
📱 iOS App: App Store
🔗 LinkedIn: KUKU
📢 Ready to make an impact? Apply now!
Skills: recommendation,machine learning,models,nlp
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