Senior Machine Learning Engineer

Role overview

Join Reddit as a Senior Machine Learning Engineer to design and build production ML systems that improve user experience and drive business outcomes. You will own the end-to-end ML lifecycle and collaborate with cross-functional teams.

Key missions

• Design and build production ML systems for core platform experiences, including:
  • Personalized recommendations
  • Search
  • Ranking systems
• Own the full ML lifecycle:
  • Problem definition
  • Feature engineering
  • Training
  • Evaluation
  • Deployment
  • Monitoring
• Research and apply state-of-the-art ML/AI, including:
  • Deep learning
  • Graph + transformer-based methods
  • LLM evaluation and alignment

Responsibilities

• Build scalable ML pipelines and model serving infrastructure.
• Improve measurable business/user metrics through applied ML.
• Translate ambiguous product or business problems into technical solutions.
• Work in low-latency and real-time production environments where needed.

Requirements

• Experience designing scalable ML pipelines, data processing systems, and model serving.
• Strong programming skills in Python and/or Java, Go, or similar languages with solid software engineering fundamentals.
• Strong ML fundamentals:
  • Classic methods (e.g., XGBoost, Random Forests, regressions)
  • Deep learning architectures (e.g., Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks: PyTorch or TensorFlow.
• 3–5+ years building, deploying, and operating production ML systems.
• Experience in one or more of the following:
  • Recommender systems
  • Search/ranking
  • Advertising/auction systems
  • Large-scale representation learning
  • Multimodal embedding systems

Nice-to-haves / additional signals

• Familiarity with distributed systems and large-scale data processing (e.g., Spark, Kafka, Ray, Airflow, BigQuery, Redis).
• Background in feature engineering, model optimization, and production monitoring.
• Experience with LLM/GenAI: evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems, and productionizing LLM-powered products at scale.
• Advanced degree in CS, ML, or a related quantitative field.