Staff Machine Learning Engineer, Consumer

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.

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.

- Relevance & recommendation systems (content, search, notifications)

- Content and user understanding & large-scale representation learning

- 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

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

- Deep 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 Transformers

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

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

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

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

- 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