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Machine Learning Engineer - Search, Ranking & Personalization
Summary by AIEngineer.careers
Machine Learning Engineer - Search, Ranking & Personalization is a Machine Learning Engineer role (full-time). with Search. in REMOTE, NEW YORK, US. Compensation shown: $190K–$260K. Imported listing (source: flexgen.zya.me). Apply on the employer's site (flexgen.zya.me).
Imported listing (source: flexgen.zya.me) · Apply on flexgen.zya.me
REMOTE · NEW YORK · US
$190K–$260K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from flexgen.zya.me
Join a fast-growing shopping platform to design, build, and scale ML systems for search, ranking, and personalization. This role involves deploying large-scale models serving hundreds of millions of items daily, and collaborating with backend and infrastructure teams. The position is fully remote, with a preference for candidates based in New York or San Francisco.
Responsibilities
- Design, train, and deploy large-scale search, ranking, and personalization models.
- Handle hundreds of millions of items daily with high performance and reliability.
- Collaborate closely with backend and infrastructure teams to integrate ML models into production (GraphQL, Prisma, Node.js, Python, gRPC/Protobuf).
- Continuously improve model accuracy and system scalability.
- Contribute to product direction and technical roadmap for the ML systems.
Requirements
- Minimum of 3+ years professional experience building and deploying ML models in production.
- Proven experience with ranking, recommendation, or personalization systems.
- Proficiency in PyTorch and large-scale data processing for real-time inference.
- Strong backend integration experience (GraphQL, Prisma, Node.js, Python, gRPC/Protobuf).
S
Search
San Francisco · US · 20+ employees
Exa is a search API specifically designed for AI agents, providing token-efficient, real-time web data and deep research capabilities. It offers structured outputs and grounded citations, enabling AI models to access high-quality data across various search verticals like company, code, and general web information.
