Latest ML Engineering jobs at Reddit

Leads technical strategy for Reddit’s Ads ML Platform, improving feature development, training-data generation, experimentation, and the path to production ML serving. The role requires 8+ years in infrastructure or distributed systems, production ML platform experience, and strong cross-team technical leadership.

Build and deploy embedding, sequence, and language-model representations for Reddit Ads, taking ML projects from requirements and experimentation through production. The role requires 5+ years of end-to-end industry ML experience, with expertise in NLP or computer vision and deep-learning frameworks.

Build and operate production machine learning systems for recommendations, search, advertising, content understanding, and LLM-powered experiences at internet scale. The role requires 3–5+ years of production ML experience, strong programming and software engineering fundamentals, and expertise with modern ML frameworks and scalable pipelines.

Design, build, and deploy production ML systems for recommendations, search, ranking, and advertising at internet scale. Own the full ML lifecycle from modeling to monitoring with strong cross-functional collaboration.

Build and operate large-scale machine learning infrastructure and models for Reddit’s recommendation and personalization systems. The role requires 5+ years of ML engineering experience, expertise in deep learning and distributed systems, and proficiency with Python and modern ML frameworks.

Leads the technical direction of large-scale ML infrastructure for embedding, recommendation, and personalization systems. The role requires 8+ years of ML engineering experience, expertise in deep learning and distributed training, and strong leadership across research, infrastructure, and production deployment.

Leads the technical direction and development of large-scale, GenAI-powered recommendation and feed-ranking systems. Requires 10+ years of industry experience in relevance-driven products, deep expertise in machine learning and recommendations, and strong organizational influence and mentoring skills.

Builds scalable experimentation, training, orchestration, and agentic AI infrastructure that accelerates Reddit’s Ads ML lifecycle. Requires 5+ years in infrastructure or distributed systems and production ML platform experience.

Build and evolve auction, bidding, and budgeting ML systems that power Reddit Ads. Design optimization algorithms balancing advertiser performance, user experience, and marketplace efficiency.

As a Senior Staff Machine Learning Engineer, you will design and build large-scale, end-to-end recommendation systems for Reddit's Notifications Relevance team. You will leverage machine learning and LLMs to deliver personalized content to users, driving growth and user engagement.

Designs and builds large-scale ML systems for Reddit's search relevance, including query understanding, retrieval, ranking, and LLM integration. Requires 10+ years in search/recommendation systems, ML model deployment, and cross-team collaboration.

Leads machine learning team developing conversion models for Reddit Ads, focusing on predictive modeling for user actions like purchases and signups. Requires deep ML expertise, ads domain knowledge, and 2+ years managing high-performing ML teams.

Leads end-to-end ML initiatives for Reddit's consumer features like recommendations, search, and AI discovery. Requires 7+ years experience with deep learning frameworks, production ML systems, and expertise in Transformers, LLMs, or recommender systems.

Builds and scales Generative AI platform infrastructure using advanced ML techniques. Requires deep expertise in large-scale model training, deployment, and optimization on cloud platforms.

Leads development of large-scale ML platforms, focusing on MLOps, graph ML infrastructure, performance optimization, and distributed training pipelines. Requires 8+ years in ML infrastructure with expertise in Python, PyTorch, Kubernetes, Ray, and cloud tools.

Leads development of large-scale ML platforms, focusing on MLOps, graph ML infrastructure, performance tuning, and distributed training optimization. Requires 5+ years in ML infrastructure with expertise in PyTorch, Kubernetes, Ray, and cloud tools.