Machine Learning Engineer

About the Role

Join Reddit's dynamic team to build and deploy cutting-edge machine learning systems that power core platform experiences and shape the future of discovery and monetization.

Responsibilities

• Design, build, and deploy production-grade machine learning models and systems at scale.
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring.
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining.
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment.
• Contribute to technical strategy, architecture, and long-term ML roadmap.

Requirements

• 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.
• Strong grasp of ML 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.
• Experience improving measurable metrics through applied machine learning.

Nice-to-Haves

• 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.
• Experience with LLM/Gen AI techniques, including LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale.