Staff Machine Learning Engineer

Reddit is a community of communities, built on shared interests and trust. They are seeking a Staff Machine Learning Engineer to lead high-impact initiatives in building and deploying large-scale machine learning systems for recommendations, search, and content understanding.

Responsibilities:

• Architect, build, and deploy large-scale ML systems powering recommendations, search, messaging, and content understanding
• Lead projects from ideation → modeling → experimentation → production → iteration
• Design and improve recommender systems and ranking models across surfaces (feed, search, notifications)
• Optimize for user engagement, discovery, and long-term value
• Build next-gen AI-powered search and recommendation experiences, including LLM-integrated systems
• Develop pipelines that help users find high-quality answers and content across Reddit’s corpus
• Build and optimize content embeddings and representation models for users, communities, and content
• Leverage and advance LLMs and multimodal models for deeper understanding and personalization
• Evaluate model performance, improve accuracy, and reduce bias
• Partner with Product, Data Science, Infra, and UX teams to solve complex problems
• Translate ambiguous business needs into scalable ML solutions
• Mentor engineers and raise the bar across the organization
• Establish best practices for ML development, experimentation, and responsible AI
• Act as a thought leader across teams and domains

Requirements:

• 6+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, 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 driving measurable impact through applied machine learning
• Subject matter expertise in Recommender systems, search systems (lexical and semantic retrieval and ranking), advertising/auction systems, large-scale representation learning, or multimodal embedding systems, content understanding etc
• 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 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