Senior Machine Learning Infrastructure Engineer (Embedding Platform)

• 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
• 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

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits- Expertise in modern deep learning architectures, including sequence models and foundational models
• 5+ years of experience in machine learning engineering, with a strong focus on large-scale ML infrastructure and recommendation or personalization systems
• Strong software engineering fundamentals, including system design, debugging, testing, and performance optimization
• Demonstrated ability to independently scope and execute ambiguous technical work, while owning high-quality implementation details
• Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar
• Experience building or scaling ML platform for large datasets and high-traffic production environments
• Solid understanding of distributed training and inference concepts, such as data parallelism, model parallelism, pipeline parallelism, or related optimization techniques
• Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders
• Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems