Machine Learning Engineer II - Reco Systems & Agentic AI

Glance AI is an AI commerce platform shaping the next wave of e-commerce with
inspiration-led shopping, less about searching for what you want and more about
discovering who you could be. Operating in 140 countries, Glance AI transforms
every screen into a stage for instant, personal, and joyful discovery, where
inspiration becomes something you can explore, feel, and shop in the moment.

Its proprietary models, seamlessly integrated with Google’s most advanced AI
platforms, Gemini and Imagen on Vertex AI, deliver hyper-realistic, deeply
personal shopping experiences across categories such as fashion, beauty, travel,
accessories, home décor, pets, and more. Designed to seamlessly integrate into
everyday consumer technology, Glance AI reimagines the future of e-commerce with
inspiration-led discovery and shopping.

With an open architecture built for effortless adoption across hardware and
software ecosystems, Glance AI is creating a platform that can become a staple
in everyday consumer technology. It partners with the world’s leading smartphone
makers, connected TV manufacturers, telecom providers, and global brands —
meeting people where they are: on mobile, smart TVs, and brand websites.

Through Glance AI’s rich first-party data and unparalleled consumer access, it
harnesses InMobi’s global scale, insights, and targeting capabilities to create
high-impact, performance-driven shopping journeys for brands worldwide. Part of
the InMobi Group, a global technology and advertising leader reaching over 2
billion devices and serving more than 30,000 enterprise brands worldwide, Glance
AI is backed by Google, Jio Platforms, and Mithril Capital.

What you will be doing

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.

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.

"Glance collects and processes personal data such as your name, contact details,
resume and other information that may contain personal data for the purpose of
processing your application. Glance utilizes Greenhouse, a third-party platform.
Please review Greenhouse's Privacy Policy to understand how the data collected
from you is processed and managed. By clicking on 'Submit Application', you
acknowledge and agree to the above privacy terms. Should you have any privacy
concerns, you may contact us through the details mentioned in your application
confirmation email."