Staff Machine Learning Engineer, Consumer

About the Role

Reddit is a community of communities, built on shared interests, passion, and trust. With millions of users submitting, voting, and commenting on topics they care about, the company requires innovative ML solutions to drive user engagement and relevance. As a Staff Machine Learning Engineer, you will be working on complex, real-world problems at massive scale, and will have the opportunity to shape the future of discovery, relevance, and monetization at Reddit.

The Consumer Engineering organization is responsible for building and maintaining the company's consumer-facing products, including the website, mobile apps, and messaging platform. As a Staff Machine Learning Engineer in this organization, you will be working closely with product, data science, and engineering teams to deliver high-impact ML solutions.

What You Will Do

• Lead end-to-end ML initiatives from ideation through production and iteration, shaping technical direction and translating product goals into scalable solutions
• Architect, build, and deploy large-scale ML systems across recommendation, search, and content/user understanding, including retrieval/ranking models, representation learning embeddings optimizations, and LLM or GenAI-powered capabilities
• Drive measurable impact on user engagement, discovery, and long-term value
• Collaborate with cross-functional teams to align product and technical roadmaps and unlock key future ML capabilities
• Stay at the forefront of AI research, evaluating and introducing new AI/ML paradigms to keep Reddit's ML ecosystem at the cutting edge
• Contribute to the development of best practices, guidelines, and ethical AI principles for responsible LLM development and deployment
• Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing
• Set technical vision and direction for the ML engineering team, ensuring alignment with company goals and objectives
• Develop and maintain large-scale ML infrastructure and pipelines, including data ingestion, processing, and storage

What We Are Looking For

• 5+ years of experience in machine learning engineering, with a focus on large-scale ML systems and infrastructure
• Expertise in recommendation algorithms, natural language processing, and computer vision
• Strong programming skills in languages such as Python, Java, or C++
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn
• Strong understanding of software engineering principles, including design patterns, testing, and deployment
• Experience with cloud-based infrastructure, including AWS, GCP, or Azure
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams
• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field

Nice to Have

• Experience with LLM or GenAI-powered capabilities, including model development and deployment
• Knowledge of open-source technology and community-driven development
• Experience with containerization using Docker, and orchestration using Kubernetes
• Familiarity with Agile development methodologies and version control systems such as Git

Benefits and Perks

• Competitive salary and equity package
• Comprehensive health, dental, and vision insurance
• Flexible PTO and paid holidays
• Remote work stipend and equipment budget
• Professional development opportunities, including conference attendance and training programs
• Access to cutting-edge technology and tools, including ML frameworks and infrastructure
• Collaborative and dynamic work environment, with a team of experienced engineers and researchers
• Opportunity to work on high-impact projects, with the potential to drive significant user and business impact

• Develop a strong portfolio of ML projects, including code repositories and research papers, to demonstrate your expertise and showcase your skills.
• Focus on developing a deep understanding of ML fundamentals, including supervised and unsupervised learning, deep learning, and neural networks.
• Practice communicating complex technical concepts to non-technical stakeholders, including product managers and designers.
• Stay up-to-date with the latest developments in AI research, including new algorithms, frameworks, and tools.
• Be prepared to discuss your experience with large-scale ML systems and infrastructure, including data ingestion, processing, and storage.
• Highlight your ability to work effectively with cross-functional teams, including product, data science, and engineering.
• Research the company's culture and values, and be prepared to discuss how your skills and experience align with these principles.