Staff Machine Learning Engineer (Consumer)

• We hire 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
• We 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 experiences
• Content understanding & representation
• Large-scale ML infrastructure and pipelines
• Lead 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 value
• Collaborate with cross-functional teams to align product and technical roadmaps and unlock key future ML capabilities
• Stay at the forefront of AI research, evaluating and introducing new AI/ML paradigms to keep Reddit’s ML ecosystem at the cutting edge
• Contribute to the development of best practices, guidelines, and ethical AI principles for responsible LLM development and deployment
• Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing
• Set technical vision and drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making
• Potential Teams
• Home Experience
• ML Understanding
• Feed Relevance
• Answer Experience
• Search and Answers Relevance
• Search Experience

Benefits

• Comprehensive health benefits

• Flexible vacation & Reddit global days off

• Family planning funds & 4+ months paid parental leave

• Personal & professional development funds

• Paid volunteer time off

• Workspace & home office benefits

• Excellent communication and collaboration skills, with the ability to discuss complex technical topics with diverse teams and translating product needs into scalable ML solutions

• 7+ years of experience building, deploying, and operating machine learning systems in production

• Expert at developing and productionizing models using TensorFlow, PyTorch, or Hugging Face Transformers

• Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc)

• Experience designing and scaling ML systems, including data pipelines, feature engineering, model training/serving, and production monitoring

• Track record of driving measurable impact through applied machine learning in real-world products

• Experience building production-quality code incorporating testing, evaluation, and monitoring using object-oriented programming, including experience in Python and Golang

• Subject matter expertise in one of the following domains:

• Recommender systems

• Search 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 environments

• Experience 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 scale

• Strong experimentation rigor, with experience formulating clear hypotheses, designing actionable learning plans and building offline/online correlations

• Advanced degree in Computer Science, Machine Learning, or related quantitative field