Machine Learning Engineer, Consumer

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 121 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

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At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning.

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.

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.

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

• 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

• 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, Tensor Flow)
• 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,…