Senior Machine Learning Infrastructure Engineer, Embedding Platform

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit redditinc. com . The LS Embedding Machine Learning Platform team is at the forefront of building highly expressive, machine learning models that power Reddit’s recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale. About the Role As a Senior Machine Learning Infrastructure Engineer , you will work across both model development and ML platform to build large-scale learning systems that improve recommendation and personalization on Reddit. At the senior level, you will own major technical components end to end: designing models, implementing training and evaluation pipelines, and driving production deployment in close partnership with ML platform, product, and cross-functional ML teams. 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. Qualifications - 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.