Staff Machine Learning Infrastructure Engineer, Embedding Platform

About the role

You will own the technical direction for large-scale machine learning platform initiatives within the LS Embedding Machine Learning Platform team at Reddit. In this role, you will guide the development of advanced deep learning architectures and high-impact ML systems that power Reddit's recommendation and personalization infrastructure. You will partner with leadership to define ML roadmaps and drive innovation in scalable model design and training approaches. Your work will ensure efficient, reliable deployment of ML models in production while mentoring and uplifting the technical capabilities of the team. You will establish and optimize real-time serving architectures for large-scale embeddings to support content discovery and user engagement at massive scale. This position offers an opportunity to influence key AI-driven systems across Reddit's ecosystem and contribute to platform growth through cutting-edge modeling. You will stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit's ML infrastructure on the leading edge.

Key facts

What you'll do

Architect and lead the development of next-generation, large-scale machine learning techniques that enhance personalization and recommendation quality.

Define and execute the ML strategy, identifying opportunities to improve content discovery, user engagement, and platform growth through advanced model design.

Lead research initiatives on scalable machine learning systems and real-time model adaptation, bringing cutting-edge advancements into production environments.

Partner with ML infrastructure teams to build high-performance, distributed training systems that efficiently scale across multiple GPUs and cloud environments.

Establish and optimize real-time serving architectures for large-scale embeddings, ensuring low-latency inference and high throughput for critical Reddit workflows.

Collaborate cross-functionally with teams in Feed Ranking, Ads, Content Understanding, and Core ML to integrate ML models into Reddit's key AI-driven systems.

Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing across the organization.

Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit's ML ecosystem cutting-edge and competitive.

Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making for platform evolution.

Champion best practices in model evaluation, A/B testing, and real-world performance measurement to ensure reliable and impactful deployments.

Optimize end-to-end ML pipelines to support scalability, maintainability, and robustness across Reddit's diverse user base and content landscape.

Promote reproducibility, observability, and monitoring across ML workflows to enable data-driven improvements and rapid iteration.

Engage with open source communities and internal stakeholders to share insights, tools, and frameworks that accelerate ML innovation at scale.

Contribute to the design of data strategies that align with modeling goals, ensuring high-quality inputs for embedding and recommendation systems.

Requirements

8+ years of experience in machine learning engineering, with a strong focus on large-scale ML systems and recommendation or personalization systems.

Expertise in modern deep learning architectures, including sequence models and foundational models, with a track record of applying them in production.

Deep understanding of complex multi-entity relationships in machine learning applications and how they are modeled in large-scale systems.

Proven ability to design, implement, and optimize scalable ML architectures, from distributed training to real-time inference in cloud environments.

Strong software engineering skills in Python, C++, or similar languages, with experience in ML infrastructure, high-performance computing, and cloud-based ML pipelines.

Demonstrated leadership in driving ML strategy, mentoring engineers, and influencing cross-functional teams to achieve ambitious technical goals.

Experience with A/B testing, model evaluation frameworks, and real-time performance monitoring to guide data-driven product decisions.

Strong communication and collaboration skills to work effectively with cross-functional stakeholders and present technical concepts to executive audiences.

Nice to have

Experience with large-scale embedding systems and serving architectures in production environments.

Background in building or optimizing distributed training pipelines for deep learning at scale.

Familiarity with Reddit's tech stack, community products, and content recommendation challenges.

Contributions to open source ML frameworks or libraries relevant to embeddings and recommendation systems.

Published research or patents in the fields of machine learning, personalization, or large-scale modeling.

Practical notes

This is a full-time position based in the United States with remote work eligibility.

The role may involve occasional travel as needed for team collaboration, company events, or business requirements.

Visa sponsorship may be considered for eligible candidates depending on role and location constraints.

Employment is contingent on successful completion of standard hiring processes and background checks.