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Machine Learning Engineer II- Recommendation Systems

0 years

0 Lacs

bengaluru karnataka india

Posted:1 week ago| Platform: Linkedin logo

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Skills Required

learning data ai design power ml ranking algorithms personalization content experimentation model training evaluation deployment collaboration engineering retrieval automate tuning reinforcement

Work Mode

On-site

Job Type

Full Time

Job Description

What you will be doing

classical machine learning

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

  • Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
  • Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
  • Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
  • Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
  • Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

  • Build and experiment with

    agentic AI systems

    that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
  • Apply

    LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models

    for semantic understanding, content classification, and user preference modeling.
  • Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
  • Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

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