Senior ML Engineer – Recommender & Search Systems (Remote)

AHU Technologies Inc

United States

Remote

USD 150,000 - 210,000

Full time

14 days+
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Job summary

AHU Technologies Inc. is seeking a Senior Machine Learning Engineer to lead end-to-end recommendation and search initiatives for a remote U.S.-based team.

You will shape feed discovery, embeddings, and ranking models to improve content delivery across product surfaces. The role requires 5+ years of production ML experience, a Bachelor’s degree from a recognized university in China, and hands-on work with two-tower models, embedding retrieval, and online/offline evaluation.

Qualifications

  • 5+ years industry experience building production ML systems with senior ownership.
  • Hands-on experience with recommendation, search, ranking, or content discovery systems.
  • Experience with consumer apps, entertainment, social, gaming, creator, or engagement-driven products.
  • Two-tower models, embedding retrieval, candidate generation, ranking, and online/offline evaluation.

Responsibilities

  • Build recommendation and search across feed, discovery, search, and content continuation.
  • Own retrieval/ranking: candidate generation, embeddings, two-tower models, features, and serving quality.
  • Design, launch, and analyze recommendation/search experiments.

Skills

Senior ownership
Strategic thinking
Cross-functional collaboration

Education

Bachelor's degree from a recognized university in China

Tools

Two-tower models
Embedding retrieval
Candidate generation
Online/offline evaluation

Job description

AHU Technologies Inc. is seeking a Senior Machine Learning Engineer to lead end-to-end recommendation and search initiatives for a remote U.S.-based team.

You will shape feed discovery, embeddings, and ranking models to improve content delivery across product surfaces. The role requires 5+ years of production ML experience, a Bachelor’s degree from a recognized university in China, and hands-on work with two-tower models, embedding retrieval, and online/offline evaluation.

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