Reddit is a community of communities. 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 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.
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 communitiesIntelligent advertising systems including ranking, bidding, measurement, and optimizationContent, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signalsLarge-scale machine learning pipelines, model serving infrastructure,



and real-time decision systemsApplied AI and LLM-driven experiences that improve relevance, discovery, and user engagementYou’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes.
Design, build, and deploy production-grade machine learning models and systems at scaleOwn the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoringBuild scalable data and model pipelines with strong reliability, observability, and automated retrainingWork 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 solutionsImprove system performance across latency, throughput, and model quality metricsResearch and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignmentContribute to technical strategy, architecture, and long-term ML roadmapBasic Qualifications 3-5+ years of experience building, deploying, and operating machine learning systems in productionStrong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentalsML Fundamentals: a robust 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.
PyTorch, TensorFlow)Experience designing scalable ML pipelines, data processing systems, and model serving infrastructureAbility to work cross-functionally and translate ambiguous product or business problems into technical solutionsExperience improving measurable metrics through applied machine learningPreferred Qualifications Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systemsFamiliarity 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 environmentsBackground in feature engineering, model optimization, and production monitoringExperience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scaleAdvanced degree in Computer Science, Machine Learning, or related quantitative fieldPotential Teams Ads Measurement ModelingAds Targeting and RetrievalAdvertiser OptimizationAds Marketplace QualityAds Creative EffectivenessAds Foundational RepresentationsAds Content UnderstandingAds RankingFeed RelevanceSearch and Answers RelevanceML UnderstandingNotifications RelevanceBenefits Comprehensive Healthcare Benefits and Income Replacement Programs401k with Employer MatchGlobal Benefit programs that fit your lifestyle, from workspace to professional development to caregiving supportFamily Planning SupportGender

📌 Senior Machine Learning engineer (Canada)
🏢 Fdetimes
📍 Canada