Role Overview

Glance AI is an AI commerce platform shaping the next wave of e-commerce with inspiration-led shopping. We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems. 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.

Responsibilities

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.

Requirements

The role requires expertise in the following areas:

Deep understanding of classical machine learning techniques.
Experience building and deploying large-scale recommendation systems.
Familiarity with agentic AI systems and autonomous ML workflows.
Proficiency with LLMs, embeddings, and retrieval-augmented architectures.
Experience with deep learning and statistical modeling for personalization.