• Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how they are computed, stored, and exposed. Provide thought leadership in user understanding and user modeling by setting a long-term technical vision and advancing the state-of-the-art in the field. • Build Foundational User Models: Lead design and implementation of advanced user models, e.g. large-scale user representation learning (sequence-based, multi-interest, multi-task) that share representations across surfaces to improve personalization experience across key Reddit products e.g. Feeds, Notification, Search and Ads, balancing latency, cost, and performance. • Reimagine user understanding with LLM/Gen-AI: Evolve user modeling beyond traditional representations by leveraging LLMs to build richer user understanding (e.g., dynamic user profiles, intent inference, semantic reasoning over user behavior). Explore how LLMs can augment or unify embeddings, attributes, and taxonomies to enable more adaptive, interpretable, and context-aware personalization. • Ship Large Scale User Understanding as a System: Partner with platform teams to design and build core components for large-scale learning and serving: storage/retrieval for embeddings, feature pipelines, and APIs. Collaborate with ML/Ranking infra to ensure low-latency serving, high availability, and integration with MLOps systems. • Drive Cross-Team Integration & Impact: Partner with Feeds, Notification, Search and Ads teams to drive experimentation and adoption of new user understanding models with product teams across Reddit, ensuring measurable end-to-end impact on key metrics. • Set Technical Bar & Mentor: Mentor senior to staff engineers, lead design reviews, steward technical decisions across the user understanding domain, and champion and drive engineering processes and best practices

• Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience • Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production • Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data • Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems • Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership

Role Description We are looking for a motivated Jr. AI ML Engineer to join our growing team. The ideal candidate is passionate about AI technologies and eager to work on real-world applications involving chatbots, automation, and AI agents. You will gain hands-on experience in building AI-powered solutions and supporting end-to-end project development—from understanding requirements to deployment and optimization. Qualifications - Bachelor's degree in Computer Science, Software Engineering, AI, Data Science, or a related field - Certifications in AI/ML or related technologies will be a plus Requirements - 6 months to 1 year of experience in AI/ML, software development, or related projects - Basic proficiency in Python with understanding of writing structured and maintainable code - Familiarity with AI/ML fundamentals and strong interest in Generative AI and LLM-based systems - Strong understanding of core ML algorithms and statistical concepts - Understanding knowledge of chatbot development, AI agents, or AI-based personal/academic projects - Exposure to AI frameworks or tools such as LangChain, LangGraph, LlamaIndex, or similar technologies - Understanding of REST APIs and how backend systems interact with applications - Awareness of concepts like embeddings, vector databases, prompt engineering, or semantic search (good to have) - Strong problem-solving skills with the ability to learn and adapt quickly in a fast-paced environment - Good communication skills and ability to work collaboratively within a team - Passion for learning new technologies and building practical AI solutions Benefits - EOBI - Provident Fund - Medical Health Insurance - Annual Leaves - Casual leaves - Sick Leaves - Bereavement Leaves - Marriage Leaves - Paternity Leaves - Maternity Leaves

• Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience • Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production • Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data • Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems • Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership