Senior Machine Learning Engineer

Job Overview

The Senior Machine Learning Engineer will own the systems that help users discover and continue engaging with content across feeds, search results, discovery surfaces, and related-content experiences.

You will design and improve candidate-generation pipelines, embedding-based retrieval, two-tower models, ranking features, and production serving systems. The role also includes designing experiments, evaluating model performance, and using user behavior to improve relevance and engagement.

Key Responsibilities

• Build recommendation and search capabilities across feeds, discovery experiences, search, and content-continuation surfaces.
• Own retrieval and ranking systems from model development through production deployment.
• Develop candidate-generation approaches for large content collections.
• Build and improve embedding-based retrieval systems.
• Design and train two-tower recommendation models.
• Create ranking features that improve content relevance and user engagement.
• Monitor and improve the quality, reliability, and performance of production model serving.
• Design, launch, and analyze recommendation and search experiments.
• Use online and offline evaluation methods to measure model effectiveness.
• Ship production improvements quickly and refine systems using observed user behavior.
• Take senior-level ownership of technical decisions and delivered machine learning capabilities.

Required Qualifications

• At least 60 months of industry experience building production machine learning systems.
• Demonstrated senior-level ownership of machine learning projects and deployed systems.
• Hands-on experience with recommendation, search, ranking, advertising ranking, feed ranking, or content-discovery systems.
• Experience working on consumer applications, entertainment platforms, social products, gaming, creator tools, or other engagement-driven products.
• Practical experience with two-tower models.
• Experience developing embedding-retrieval systems.
• Experience with candidate generation and ranking pipelines.
• Knowledge of online and offline model-evaluation methods.
• Ability to provide clear examples of recommendation or search systems successfully shipped to production.
• Professional fluency in Chinese or Mandarin.
• U.S. citizenship.

Strong-Fit Experience

• Building recommendation and search systems for content feeds.
• Improving discovery and content-continuation experiences.
• Owning retrieval and ranking architecture.
• Developing embeddings, ranking features, and model-serving infrastructure.
• Designing controlled experiments for recommendation or search improvements.
• Working with products similar to short-form video, streaming media, social content, or creator platforms.
• Operating effectively in an ownership-heavy startup environment.

Core Technical Focus Areas

• Recommendation systems.
• Search ranking.
• Feed ranking.
• Content discovery.
• Candidate generation.
• Embedding retrieval.
• Two-tower models.
• Machine learning experimentation.
• Online and offline evaluation.
• Production model serving.

Work Arrangement

• Remote position within the United States.
• The role operates in a fast-moving startup environment.
• Candidates should be comfortable taking significant individual ownership.
• The company expects engineers to ship quickly, learn from users, and help define an emerging product category.

Compensation

The stated annual salary range is $150,000 to $300,000.

The compensation package also includes equity. The posting does not provide additional details about equity value, vesting, benefits, or incentive eligibility.

Interview Process

1. Initial WeKruit screening.
2. Technical and product-focused discussion covering senior machine learning engineering, recommendation systems, and relevant shipped work.
3. Conversation with the team or company founders.

About the Product

The company is developing a consumer platform at the intersection of generative artificial intelligence, social products, mobile technology, and interactive entertainment.

Its product direction is based on the idea that digital content will increasingly become playable, remixable, and interactive through AI-generated mini applications. The company has reportedly raised $30 million in Series A funding.