Applied Scientist III - Recommendations

Glance is looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems. The role involves designing, building, and deploying intelligent systems that power Glance's personalized lock screen and live entertainment experiences, blending deep ML craftsmanship with innovation in autonomous/agentic systems.

Responsibilities

Design 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; Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance's consumer experiences; Contribute to Glance's ML/AI thought leadership, including blogs, case studies, internal tech talks, and industry conferences; Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities

Requirements

Bachelor's/master's in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields; 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization; Experience with large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings); Classical ML and deep learning techniques across NLP, sequence modelling, RL, clustering, and time series; Experience in deploying ML workflows/models in production systems; Big data processing (Spark, distributed data systems) and cloud computing; Designing end-to-end ML solutions from prototype to production; Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets; Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.); Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI); Comfortable working in cross-functional teams; Excellent communication skills with the ability to simplify complex technical concepts

Nice to have

Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents); PhD; Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems; Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android); Strong bias toward experimentation and production impact; Curiosity and problem-solving ability