Staff Machine Learning Engineer, Ads Foundational Representations
Reddit · New York City, NY
About this role
**Staff Machine Learning Engineer — Ads Foundational Representations (AFR)** **Location**: Flexible-first. Remote from anywhere on the US East Coast (Eastern Time) (NY office optional). Reddit is a community of communities—home to open, authentic conversations. With 100,000+ active communities and ~130M daily active unique visitors, Reddit is a major source of information. The **Ads Foundational Representations (AFR)** team builds signals and representations for Reddit’s core entities (ads, posts, users, etc.) to power Reddit Ads. You’ll help create embeddings that capture semantic, contextual, and behavioral information—especially for content and user interests. ### What you’ll work on - **Multimodal Content Embeddings**: Embed organic and promoted text/media into a shared space. - **Contextual & Behavioral Relevance**: Define and measure ad relevance; fine-tune embeddings to better reflect relevance. - **Knowledge Graph Embeddings**: Build representations for knowledge graph entities (e.g., brands/IP) for high-precision targeting and business insights. - **User Intent Modeling**: Create user representations from interacted content (batch/real-time sequence modeling, LLM summarization, etc.). - **LLM-based Representations**: Use LLMs/VLMs/foundational models to build complex representations that improve ranking outcomes. These signals support the Ads Delivery funnel (targeting → auction) and downstream products like Business Insights and advertiser-facing tools (e.g., creative generation/optimization). ### Role overview As a **Staff ML Engineer**, you’ll set the technical direction across multiple pillars. You’ll lead cross-functional ML projects end to end—from business gap analysis through engineering execution. - ~**50%** technical leadership & mentorship (strategy/designs, cross-functional collaboration, raising the quality bar) - ~**50%** hands-on work (data analysis/engineering, modeling, automation) ### Responsibilities - Provide technical leadership and mentorship to MLEs (design reviews, best practices, maintain high standards) - Partner with leadership to develop technical strategy for content-based embeddings & ad relevance - Build/iterate embedding models for ad use cases (e.g., aggregation pipelines, two-tower architectures, sequence models) - Work with local and 3rd-party LLMs/VLMs (representations, evaluation methods, prompt tuning/fine-tuning) - Build data processing and inference pipelines - Perform qualitative/quantitative evaluation and end-to-end experimentation (benchmarks → offline recommender metrics → online experiments) - Ensure reliability, scalability, and performance (automated tests, monitoring, model management best practices) - Participate in modeling/coding reviews and provide feedback - Collaborate with cross-functional teams to translate business requirements into technical solutions ### Required Qualifications - **7+ years** hands-on experience across the full ML lifecycle (design, training, evaluation, testing, deployment) - **Staff-level technical leadership**: mentor engineers, drive standards, lead complex cross-functional projects without direct people-management authority - Strong communication skills (translate complex technical concepts to different audiences) - Track record working on large-scale content-rich **NLP/CV** problems and using embeddings effectively - Data-driven approach and comfort going beyond surface-level metrics - Familiarity with **Ads** and/or **Search/Recommender
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