Machine Learning Engineer

Join Reddit as a Machine Learning Engineer and work on high-impact problems across the platform. You will design and build production ML systems that power personalized recommendations, intelligent advertising systems, and large-scale machine learning pipelines. You will own the full ML lifecycle, work with large-scale datasets, and partner cross-functionally with various teams. This role offers comprehensive health benefits, flexible vacation, family planning funds, and personal and professional development funds.

• Design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, and ranking systems.
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring.
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment.

• 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
• Experience improving measurable metrics through applied machine learning
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• 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)
• 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, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field