ML Engineer (AI)

Location: Remote, China; interviews may be conducted virtually and/or onsite.

Company

hirify.global is building AI-native applications that bring proactive intelligence to communication, coordination, errands, and workflows.

What you will do

• Build and improve machine learning models for classification, ranking, retrieval, recommendation, and personalization.
• Optimize classifiers and ranking systems for accuracy, precision, relevance, latency, cost, and reliability.
• Improve search relevance and context retrieval using embeddings, vector search, hybrid search, and reranking.
• Develop evaluation pipelines, labeled datasets, and metrics for measuring model quality and detecting regressions.
• Build and maintain training, inference, and deployment pipelines, including model fine-tuning and adaptation.
• Debug production issues, monitor model performance, and ship improvements based on real-world feedback.

Requirements

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying machine learning models in production.
• Experience with classification, ranking, retrieval, recommendation systems, or personalization.
• Proficiency in Python and experience with PyTorch or JAX.
• Understanding of model evaluation, experimentation, performance metrics, embeddings, vector search, and production inference pipelines.
• Ability to write production-quality code and balance model quality, latency, cost, and reliability.

Culture & Benefits

• High-talent-density, hands-on team with collective decision-making and fast execution.
• Work environment emphasizes structure, independent judgment, ownership, and continuous learning.
• Remote work with virtual and/or onsite interview options.
• Technical team members evaluate applications and the process is designed for a prompt decision.

Hiring process

• Applications are evaluated by technical team members.
• Selected candidates complete 3, and no more than 4, interviews.
• Interviews are conducted virtually and/or onsite, followed by a prompt hiring decision.