Software Engineer, Machine Learning - Ranking, Home Feed, Standalone Apps Team

We're Meta's Standalone Apps Team — we build new apps to reach new user scenarios and serve needs that aren't well met today. The project we're building right now is an image-and-text community where real people share first-hand experiences and recommendations — what to try, where to go, how to do something. It's where people come to get inspired and to make everyday decisions, learning from others who've actually been there. It's still early — a small team working 0→1. If you want high ownership, a short path from idea to ship, and the chance to shape a product from its first version, this is that kind of team. We're looking for a staff-level ML engineer to own ranking for the home feed. You'll set the modeling and systems direction for the feed that defines the product experience, and drive measurable gains in engagement and quality.

Responsibilities

• Own the modeling and systems direction for home-feed ranking — candidate generation, ranking models, features, and the serving stack.
• Define the metrics and experimentation approach for feed quality; drive measurable improvements while guarding against low-quality content.
• Build and scale the ML systems (training, features, serving) that ranking depends on.
• Set technical direction across teams; align ranking work with product strategy.
• Mentor ML and product engineers; raise the bar on modeling and experimentation rigor.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of relevant experience, with deep expertise in recommendation systems / ranking
• Strong foundation in ML (modeling, features, evaluation) and in the systems that train and serve models at scale
• Track record of driving ranking/recsys improvements that moved product metrics
• Experience setting technical direction across multiple teams Experience building ranking for a new product or cold-start feed (0→1, sparse data)
• Experience with large-scale recommendation infrastructure and online experimentation
• Publications or recognized contributions in recsys/ML