Staff Machine Learning Engineer, Embeddings Platform

About the Role

The LS Embedding Machine Learning team is at the forefront of building highly expressive machine learning models that power Reddit's recommendation systems. The team's work goes beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit's ecosystem. As a Staff Machine Learning Engineer, you will be responsible for architecting and leading the development of next-generation, large-scale machine learning techniques, and defining and executing the ML strategy to enhance personalization and recommendation quality across Reddit.

What You Will Do

• Architect and lead the development of next-generation, large-scale machine learning techniques
• Define and execute the ML strategy, identifying opportunities to enhance personalization and recommendation quality across Reddit
• Lead research initiatives on scalable machine learning systems and real-time model adaptation, bringing cutting-edge advancements into production
• 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
• 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
• Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit's ML ecosystem cutting-edge
• Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making

What We Are Looking For

• 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
• 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, with a focus on personalization and recommendation systems
• Strong collaboration and leadership skills, with experience mentoring and guiding senior and mid-level ML engineers
• Excellent communication and presentation skills, with the ability to drive technical discussions and present findings to leadership
• Strong understanding of machine learning infrastructure, including distributed training systems and real-time serving architectures

Nice to Have

• Experience with Excel and data analysis tools
• Familiarity with cloud environments, including AWS or Google Cloud
• Experience with containerization and orchestration tools, such as Docker and Kubernetes
• Knowledge of machine learning frameworks, including TensorFlow or PyTorch

Benefits and Perks

• Competitive salary and equity package
• Comprehensive health, dental, and vision insurance
• Flexible PTO and vacation policy
• Remote work stipend and home office setup support
• Professional development opportunities, including conference attendance and training programs
• Access to cutting-edge machine learning tools and technologies
• Collaborative and dynamic work environment with a team of experienced machine learning engineers

• Tip: Highlight your experience with large-scale machine learning systems and personalization platforms in your resume and cover letter.
• Be prepared to discuss your approach to designing and optimizing scalable ML architectures, and how you have applied this expertise in previous roles.
• Showcase your ability to collaborate and lead cross-functional teams, including experience mentoring and guiding senior and mid-level ML engineers.
• Familiarize yourself with Reddit's machine learning infrastructure and technologies, including distributed training systems and real-time serving architectures.
• Be ready to discuss your understanding of complex multi-entity relationships in machine learning applications, and how you have modeled these relationships in large-scale systems.
• Consider creating a portfolio of your work, including examples of machine learning models you have developed and deployed, to demonstrate your expertise to the hiring team.