Join a high-impact engineering team building the foundation for search, ranking, and personalization at scale. You'll develop machine learning models that process hundreds of millions of items daily, ensuring fast, accurate, and relevant results for a broad user base. This role is central to shaping how users discover content and interact with the platform.
What You’ll Do
- Design, train, and deploy machine learning models focused on search relevance, ranking, and personalized experiences.
- Optimize systems for performance, reliability, and real-time inference at scale.
- Collaborate with backend and infrastructure teams to integrate models using GraphQL, Prisma, Node.js, Python, and gRPC/Protobuf.
- Iterate on model accuracy and system efficiency based on data-driven insights.
- Help define the long-term vision and technical roadmap for machine learning capabilities.
What We’re Looking For
You have at least three years of experience deploying ML solutions in production environments, with direct work in ranking, recommendations, or personalization systems. Proficiency in PyTorch and real-time data processing is essential. You’re comfortable integrating models into backend services and thrive in a fast-paced, builder-oriented culture.
Experience with consumer-facing search or recommendation products is a strong plus, especially at companies with large-scale data challenges. Prior work at tech-first consumer platforms will set you apart.
Environment & Expectations
This role operates in a high-intensity startup environment that values rapid experimentation, end-to-end ownership, and intrinsic motivation. You should be comfortable working independently, shipping quickly, and iterating based on real-world feedback. The position is hybrid, with primary locations in New York and San Francisco, and remote flexibility for candidates based in those areas.
Compensation & Benefits
Base salary ranges from $190,000 to $260,000 annually, with a competitive equity package. You’ll work alongside seasoned engineers from top consumer tech companies and help build core ML systems from the ground up, directly influencing product strategy and technical direction.
