Staff Machine Learning Engineer, Consumer

Details

Reddit is hiring a Staff Machine Learning Engineer to help drive the next generation of its machine learning ecosystem across recommendations, search, messaging, and foundational AI systems. The role involves leading high-impact initiatives from ideation to production while shaping technical strategy and product direction across multiple machine learning domains.

Responsibilities

• Lead end-to-end machine learning initiatives from ideation through production and iteration.
• Shape technical direction and translate product goals into scalable solutions.
• Architect, build, and deploy large-scale machine learning systems for recommendation, search, and content and user understanding.
• Develop retrieval and ranking models, representation learning and embedding optimizations, and LLM- or GenAI-powered capabilities.
• Drive measurable impact on user engagement, discovery, and long-term value.
• Collaborate with Product, Data Science, and Engineering to align product and technical roadmaps.
• Evaluate and introduce new AI and machine learning paradigms.
• Contribute to best practices, guidelines, and ethical AI principles for responsible LLM development and deployment.
• Mentor and guide senior and mid-level machine learning engineers.
• Set technical vision, lead technical discussions, present findings to leadership, and contribute to long-term machine learning planning and decision-making.

Requirements

• 7+ years of experience building, deploying, and operating machine learning systems in production.
• Deep understanding of classical machine learning methods and modern deep learning, including Transformers and graph neural networks.
• Expert experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers.
• Experience writing production-quality, object-oriented code with testing, evaluation, and monitoring, including Python and Golang.
• Experience designing and scaling machine learning systems, including data pipelines, feature engineering, model training and serving, and production monitoring.
• Excellent communication and collaboration skills, with the ability to explain complex technical topics and translate product needs into scalable machine learning solutions.
• Track record of delivering measurable impact through applied machine learning in real-world products.

Preferred Qualifications

• Subject matter expertise in recommender systems, search systems, or content understanding, including NLU, NLP, LLMs, topic and taxonomy modeling, interest graphs or clustering, and multimodal understanding.
• Familiarity with distributed systems and large-scale data processing frameworks such as Spark, Kafka, Ray, Airflow, BigQuery, and Redis.
• Experience with real-time systems and low-latency production environments.
• Experience with LLM and GenAI techniques, including LLM evaluation, alignment, fine-tuning, knowledge distillation, retrieval-augmented generation, agentic systems, and productionizing LLM-powered products at scale.
• Strong experimentation rigor, including hypothesis formulation, actionable learning plans, and offline-to-online correlation analysis.
• Advanced degree in Computer Science, Machine Learning, or a related quantitative field.

Potential Teams

• Home Experience
• ML Understanding
• Feed Relevance
• Answer Experience
• Search and Answers Relevance
• Search Experience

Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefits supporting workspace, professional development, and caregiving.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.
• Equity in the form of restricted stock units may be available, and some positions may also be eligible for commission.

Compensation

The base salary range is $230,000–$322,000 USD. Final offers depend on factors including skills, depth of experience, and relevant licenses or credentials.