What you will be doing

We are looking for a Applied 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 System

- sDesign and develop large-scale recommendation systems using advanced ML, statistical modelling, 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

**. Cross-functional impa**
- ctCollaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance’s consumer experience
- s.Contribute to Glance’s ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conference
- s.Thrive in a multi-functional, highly team-oriented team environment with engineering, product, business, and creative team
- s.Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic prioritie

s.We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI system **s.

You should have experience wi**





- th:Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embedding
- s).Classical ML and deep learning techniques across NLP, sequence modelling, RL, clustering, and time seri
- es.Experience in deploying ML workflows/models in production sys
- temBig data processing (Spark, distributed data systems) and cloud computi
- ng.Designing end-to-end ML solutions—from prototype to producti
- on.Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agent

s).We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impa ct.Our team includes engineers, physicists, economists, mathematicians, and social scientists—a great data scientist can come from anywhe **re.

Qualificat**

- ionsBachelor’s/master’s in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a p
- lus.8.5+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalizat
- ion.Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desira
- ble.Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datas
- ets.Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, et
- c.).Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex
- AI).Comfortable working in cross-functional te
- ams.Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Andro
- id).Excellent communication skills with the ability to simplify complex technical conce

pts.

📌 Staff Applied Scientist Recommendation (Bangalore Urban)
🏢 Glance
📍 Bangalore Urban