The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users’ interests based on the content they engage with
Our team has the potential to highlight one of Reddit’s biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas:
Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space
Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine-tuning embeddings to better reflect relevance
Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights
User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: batch & real-time sequence modeling, LLM summarization, etc
LLM-based Representations - Leveraging LLMs, VLMs, and foundational models to build complex representations of Reddit entities that improve ranking outcomes
The signals and features we create become a key piece in the Ads Delivery funnel, from targeting to the auction, as well as the Business Insights product and other advertiser-facing products such as Creative generation and optimization
As a Senior ML Engineer, you’ll be in charge of the full-cycle execution of ML projects - from collaborating with cross-functional teams on requirements and design, to the implementation of the feature and its experimentation
Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models
Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings
Building data processing and inference pipelines for the models we develop
Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments
Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management
Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team’s standards for quality and performance
Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions
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- Demonstrated Staff-level technical leadership: mentoring engineers, driving standards and bar raising, leading complex cross-functional projects: from requirements, design to cross-team/functional alignment and execution without direct people-management authority
Strong track record of working on content rich NLP/CV problems at scale, and using embeddings as a tool to solve them
Established data-driven approach for ML system development. Excitement about working with data and readiness to look behind the metric numbers
Familiarity with the Ads domain and/or Search/Recommender systems
Excellent communication skills, with the ability to translate complex technical concepts to different audiences, both verbally and in writing
Experience with mainstream DL frameworks: PyTorch or TensorFlow
7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models
Tech leadership experience: mentoring junior engineers and leading complex projects
