Applied Scientist II- Recommendation Systems

Job description

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 Systems • 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. Cross-functional impact • 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—blogs, case studies, internal tech talks, and industry conferences. • Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams. • Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities. We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems. You should have 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 system • Big data processing (Spark, distributed data systems) and cloud computing. • Designing end-to-end ML solutions—from prototype to production. • Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents). Qualifications • Bachelor’s/master’s in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus. • 3+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization. • Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable. • 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. • Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android). • Excellent communication skills with the ability to simplify complex technical concepts. We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact. Our team includes engineers, physicists, economists, mathematicians, and social scientists—a great data scientist can come from anywhere.