Senior Machine Learning Engineer

Role overview The Senior Machine Learning Engineer owns search and recommendation systems for a consumer platform that surfaces user-generated games created through AI chat tools and discovered through a personalized feed. This is a senior individual contributor role focused on building and shipping production ranking, retrieval, and discovery systems for a fast-moving catalog of new content. It suits an experienced ML engineer who has owned real recommendation or search systems end to end and can operate independently in an ambiguous product environment.

Responsibilities - Build and improve recommendation and search systems across feed, discovery, search, and continuation surfaces. - Own retrieval and ranking components, including candidate generation, embeddings, two-tower models, feature engineering, and serving quality. - Design, launch, and analyze experiments, then iterate based on measurable outcomes. - Improve cold-start quality for new users and newly published content. - Build user, content, creator, and session representations from behavioral signals. - Partner with product and engineering on metrics, experimentation strategy, and content distribution. - Ship practical ML systems with monitoring, clear evaluation, and reliable production behavior.

Requirements - 5+ years building production ML systems with senior ownership of recommendation, search, or ranking. - Hands-on experience with consumer-facing recommendation or search at scale. - Strong intuition for relevance, retention, engagement, and content distribution. - Solid engineering skills spanning modeling, data pipelines, backend integration, and online serving. - High ownership and clear communication in ambiguous, fast-moving product environments.

Nice to have - Experience with LLM-powered ranking, semantic search, or multimodal content understanding. - Background in UGC, creator marketplaces, or rapidly changing content catalogs. - Familiarity with explore/exploit, bandits, or long-term value optimization. - Startup or 0-to-1 ML infrastructure experience.

Benefits and work setup - Competitive salary plus meaningful equity. - Remote-first team with full-time U.S.-based remote work.