Senior Machine Learning Engineer

About the Role

At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with conversations across the globe. We hire Machine Learning Engineers across both our Consumer and Ads organizations to build systems end-to-end—from research and modeling to production deployment.

What You'll Work On

• Personalized recommendations, search, and ranking systems that help users discover relevant content and communities.
• Intelligent advertising systems including ranking, bidding, measurement, and optimization.
• Content, Advertisers, and User understanding, from foundational representations to signal derivation.
• Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems.
• Applied AI and LLM-driven experiences that enhance relevance, discovery, and user engagement.

What You'll Do

• Design, build, and deploy production-grade machine learning models and systems at internet scale.
• Own the full ML lifecycle: problem definition, feature engineering, training, evaluation, deployment, and monitoring.
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining.
• Work with large-scale datasets across ranking, recommendations, search relevance, prediction, and optimization.
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams.
• Optimize system performance across latency, throughput, and model quality metrics.
• Research and apply state-of-the-art ML and AI techniques (deep learning, graph & transformer-based models, LLM evaluation/alignment).
• Contribute to technical strategy, architecture, and the long-term ML roadmap.

Basic Qualifications

• 3-5+ years of experience building, deploying, and operating machine learning systems in production.
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals.
• ML Fundamentals: 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.
• Demonstrated experience improving measurable metrics through applied ML.

Preferred Qualifications

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems.
• Familiarity with distributed systems and large-scale data processing (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 (evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems, and productionizing at scale).
• Advanced degree in Computer Science, Machine Learning, or related quantitative field.

Interview Process Note

In select roles and locations, interviews will be recorded, transcribed, and summarized by artificial intelligence (AI). Candidates have the opportunity to opt out prior to scheduled interviews.