Senior Machine Learning Infrastructure Engineer, Embedding Platform

Details

Reddit's LS Embedding Machine Learning Platform team builds expressive machine learning models and scalable systems that power recommendation and personalization across Reddit. The role focuses on model development, ML platform engineering, training and evaluation pipelines, and production deployment for large-scale learning systems.

Responsibilities

• Design, train, and improve large-scale machine learning platforms for recommendation and personalization systems.
• Own major ML system components end to end, from problem framing through production rollout.
• Build and optimize ML pipelines covering 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.
• Develop reliable serving and monitoring patterns for low-latency, high-throughput production ML systems.
• Collaborate with product, relevance, ads, core ML, and other cross-functional teams to deliver measurable improvements in user experience and business impact.
• Drive offline and online evaluation, experimentation, model diagnostics, and feedback-loop improvements.
• Contribute to engineering quality through 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 platforms for large datasets and high-traffic production environments.
• Ability to independently scope and execute ambiguous technical work while owning high-quality implementation details.
• Solid understanding of distributed training and inference concepts, including 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 present complex ML concepts to technical and non-technical stakeholders.

Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefit programs 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.

Compensation

The base salary range is $190,800–$267,100 USD. The role may also be eligible for commission depending on the position offered.