Staff Machine Learning Engineer, Consumer

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 126 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 are hiring Machine Learning Engineers across our Consumer Engineering organization, giving you the opportunity to work on a wide range of high-impact problems across the Consumer 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 OnWe are looking for a Staff Machine Learning Engineer to help drive the next generation of Reddit’s ML ecosystem across recommendations, search, messaging, and foundational AI systems. You will lead high-impact initiatives from ideation to production, shaping both technical strategy and product direction across multiple ML domains. This is a highly cross-functional role partnering with Product, Data Science, and Engineering to deliver meaningful user and business impact.This role sits at the intersection of:Relevance & recommendation systems (content, search, notifications)AI-powered discovery & LLM-driven experiencesContent and user understanding & large-scale representation learningLarge-scale ML infrastructure and pipelinesWhat You’ll DoLead end-to-end ML initiatives from ideation through production and iteration, shaping technical direction and translating product goals into scalable solutions Architect, build and deploy large-scale ML systems across recommendation, search, and content/user understanding, including retrieval/ranking models, representation learnings embeddings optimizations, and LLM or GenAI-powered capabilities Drive measurable impact on user engagement, discovery, and long-term valueCollaborate with cross-functional teams to align product and technical roadmaps and unlock key future ML capabilitiesStay at the forefront of AI research, evaluating and introducing new AI/ML paradigms to keep Reddit’s ML ecosystem at the cutting edgeContribute to the development of best practices, guidelines, and ethical AI principles for responsible LLM development and deploymentMentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharingSet technical vision and drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-makingRequired Qualifications7+ years of experience building, deploying, and operating machine learning systems in productionDeep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc)Expert at developing and productionizing models using TensorFlow, PyTorch, or Hugging Face TransformersExperience building production-quality code incorporating testing, evaluation, and monitoring using object-oriented programming, including experience in Python and GolangExperience designing and scaling ML systems, including data pipelines, feature engineering, model training/serving, and production monitoringExcellent communication and collaboration skills, with the ability to discuss complex technical topics with diverse teams and translating product needs into scalable ML solutionsTrack record of driving measurable impact through applied machine learning in real-world productsPreferred QualificationsSubject matter expertise in one of the following domains:Recommender systemsSearch systems (lexical and semantic retrieval and ranking)Content understanding (NLU/NLP/LLM, topic/taxonomy modeling, interest graphs or clustering, and multimodal understanding)Familiarity with distributed systems and large-scale data processing frameworks (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)Experience working with real-time systems and low-latency production environmentsExperience with LLM/GenAI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scaleStrong experimentation rigor, with experience formulating clear hypotheses, designing actionable learning pl