Senior Machine Learning Engineer

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning. We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit. If you love working on complex, real-world ML problems at massive scale, this role is for you. What You’ll Work On As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including: Personalized recommendations, search, and ranking systems that help users discover the most relevant content and communities Intelligent advertising systems including ranking, bidding, measurement, and optimization Content, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signals Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems Applied AI and LLM-driven experiences that improve relevance, discovery, and user engagement You’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. What You’ll Do Design, build, and deploy production-grade machine learning models and systems at scale Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring Build scalable data and model pipelines with strong reliability, observability, and automated retraining Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems. Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions Improve system performance across latency, throughput, and model quality metrics Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment Contribute to technical strategy, architecture, and long-term ML roadmap Basic Qualifications 3-5+ years of experience building, deploying, and operating machine learning systems in production Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow) Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions Experience improving measurable metrics through applied machine learning Preferred Qualifications Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.) Experience working with real-time systems and low-latency production environments Background in feature engineering, model optimization, and production monitoring Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning,