Senior Machine Learning Infrastructure Engineer, Embedding Platform

Reddit, Inc. is a community platform built around shared interests, discussion, and user-generated content, serving millions of daily visitors. The Senior Machine Learning Infrastructure Engineer will build and scale machine learning systems for recommendation and personalization, owning model development, training and evaluation pipelines, deployment, serving, and monitoring. The role partners with product and machine learning teams to improve user experience and business impact at scale.

Responsibilities:

• Design, train, and improve large-scale machine learning platforms for recommendation or personalization systems
• Own and deliver major ML systems components end to end, from problem framing through production rollout
• Build and optimize end-to-end ML pipelines spanning data preparation, feature generation, training, evaluation, and deployment
• Improve distributed training, model efficiency, and online inference performance
• Apply modern modeling approaches including sequence modeling and related foundation-model techniques to Reddit use cases
• Develop reliable serving and monitoring patterns for low-latency, high-throughput production ML systems
• Work with cross-functional partners across product, relevance, ads, and core ML teams to deliver measurable improvements in user experience and business impact
• Drive rigorous offline and online evaluation, including experimentation, model diagnostics, and feedback-loop improvement
• Contribute to engineering quality through strong code, design reviews, documentation, and operational excellence

Requirements:

• 5+ years of experience in machine learning engineering, with a strong focus on large-scale ML infrastructure and recommendation or personalization systems
• Expertise in modern deep learning architectures, including sequence models and foundational models
• Experience building or scaling ML platform for large datasets and high-traffic production environments
• Demonstrated ability to independently scope and execute ambiguous technical work, while owning high-quality implementation details
• Solid understanding of distributed training and inference concepts, such as data parallelism, model parallelism, pipeline parallelism, or related optimization techniques
• Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar
• Strong software engineering fundamentals, including system design, debugging, testing, and performance optimization
• Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems
• Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders