Machine Learning Engineer III - Recommendation Systems

What you will be doing

classical machine learning

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.

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 modelling.
• 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.

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.
• 6+ 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.