Staff Machine Learning Engineer- I

• *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.

• *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.

• *The experience we need**

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 modeling, 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).

We value curiosity, problem\-solving ability, and a strong bias toward experimentation and production impact.

• *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.

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