Recommendation Systems Engineer

About Playabl

Playabl is the platform for user-generated games. Creators make games by chatting with AI, publish them, and players discover them in a feed.

We're a small team backed by Y Combinator, with millions of plays across tens of thousands of user-generated games, all organic.

About the Role

We are looking for a Recommendation Systems Engineer to build the personalization infrastructure behind Playabl's game feed. This is an end-to-end engineering role. You will help determine what we need to understand about our users and games, build the data foundation required to support that understanding, and develop the systems that select and rank the right games for each user.

What You Will Own

You will own and develop key parts of Playabl's recommendation system, including:

• A personalized game feed that adapts to each user's interests and behavior.
• User-interest profiles built from signals such as impressions, playtime, completion, skips, likes, shares, remixes, and creation activity.
• Representations of games based on their mechanics, genre, difficulty, visual style, content, audience, and player behavior.
• Data pipelines and warehouse models for reliable user, game, and interaction data.
• Candidate retrieval, filtering, ranking, and feed-serving systems.
• Online features, caching, and low-latency infrastructure required to serve recommendations in real time.
• Experimentation and evaluation systems for measuring recommendation quality and product impact.
• Cold-start, content discovery, diversity, and creator-distribution problems.

Responsibilities

• Define the events, metadata, and behavioral signals needed to understand users and games.
• Design data models and pipelines that transform raw activity into reliable recommendation features.
• Build and maintain user profiles, game profiles, and user-game interaction datasets.
• Develop candidate-generation and ranking approaches, beginning with practical systems and increasing their sophistication over time.
• Productionize recommendation models and integrate them into Playabl's feed infrastructure.
• Build efficient online-serving and caching strategies that keep the feed fast and scalable.
• Establish offline evaluation, online experimentation, monitoring, and model-performance reporting.
• Work closely with product, data, game, and backend teams to translate user behavior into better discovery.
• Balance relevance with exploration, diversity, freshness, and fair opportunities for new creators.

What We Are Looking For

• Strong software engineering fundamentals and experience building production backend or data systems.
• Experience with recommendation systems, search, ranking, personalization, or a closely related field.
• Strong understanding of data pipelines, data warehouses, event instrumentation, and analytical data modeling.
• Experience working with behavioral data and transforming it into usable features.
• Understanding of retrieval, ranking, embeddings, collaborative filtering, or learning-to-rank approaches.
• Familiarity with online model serving, caching, latency, and distributed systems.
• Experience designing experiments and evaluating recommendation changes using both offline and online metrics.
• Strong product judgment and the ability to connect technical decisions to user and creator outcomes.
• Ability to work across data, machine learning, backend infrastructure, and product implementation.

Strong Advantages

• Experience building feeds or discovery systems for gaming, social, media, marketplace, or other consumer products.
• Experience with real-time or near-real-time data processing.
• Experience with vector search, approximate nearest-neighbor retrieval, or feature-store infrastructure.
• Experience handling cold-start, sparse interaction data, and rapidly changing content catalogs.
• Familiarity with technologies such as Python, SQL, Redis, Kafka, Spark, OpenSearch, Elasticsearch, or similar systems.
• Experience taking recommendation systems from an early rules-based stage to a mature machine-learning system.
• A strong interest in games, creator ecosystems, and interactive content.

What Success Looks Like

You will establish the technical and data foundation for Playabl to understand what each user wants to play and what each game has to offer. Over time, the feed should become more relevant with every interaction, help users discover games they would not have found otherwise, and give high-quality new games and creators a meaningful opportunity to reach the right audience.

How to Apply

Please share your CV and examples of recommendation, ranking, search, machine learning, backend, or data systems you have designed or built. We are particularly interested in candidates who can explain their contribution across the full lifecycle: data collection, feature development, modeling, production serving, and evaluation.