About Reddit
Reddit is a community of communities, built on shared interests, passion, and trust. It is home to open and authentic conversations, with over 100,000 active communities and approximately 126 million daily active unique visitors, making it one of the internet’s largest sources of information.
The Role of Machine Learning at Reddit
At Reddit, machine learning is central to how millions discover, connect, and engage. Our ML engineers solve high-impact problems in large-scale applied machine learning, from personalized recommendations and search to optimizing advertising and marketplace dynamics. We are expanding our Consumer Engineering organization and seeking Machine Learning Engineers who can build systems end-to-end, from research and modeling to production deployment, shaping the future of discovery, relevance, and monetization.
What You’ll Work On
As a Staff Machine Learning Engineer, you will 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 technical strategy and product direction across multiple ML domains. This highly cross-functional role involves partnering with Product, Data Science, and Engineering to deliver significant user and business impact. The role sits at the intersection of:
- Relevance & recommendation systems (content, search, notifications)
- AI-powered discovery & LLM-driven experiences
- Content and user understanding & large-scale representation learning
- Large-scale ML infrastructure and pipelines
What You’ll Do
- 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 learning 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Potential Teams
- Home Experience
- ML Understanding
- Feed Relevance
- Answer Experience
- Search and Answers Relevance
- Search Experience