Machine Learning Engineer (Recommendations & Personalization)

Location: Shenzhen, China; on-site

Company

hirify.global develops a personalized shopping experience and is pursuing global scale.

What you will do

• Own recommendation and personalization systems end-to-end, including datasets, training, offline evaluation, A/B testing, rollout, serving, monitoring, retraining, and rollback.
• Develop recommendation, ranking, user modeling, CRM intelligence, and search-related machine learning solutions.
• Apply classic machine learning, embedding models, fine-tuned open-source models, and LLM-based techniques based on measured performance.
• Build evaluation sets and experiment harnesses, and run A/B tests, interleaving experiments, and causal analysis.
• Work with product, operations, and CRM stakeholders to improve business and product metrics.
• Mentor ML engineers and contribute to modern recommendation and LLM practices.

Requirements

• Experience shipping and iterating recommendation, personalization, ranking, or search systems serving millions of users; typically 2+ years of industrial ML experience.
• Strong understanding of retrieval and ranking models, embeddings, and online experimentation.
• Hands-on experience fine-tuning open-source models and LLMs with SFT, LoRA, or DPO for ranking, personalization, or user modeling.
• Strong Python and PyTorch skills, with quantitative education in computer science, statistics, mathematics, or equivalent practical experience.
• Solid MLOps knowledge, including data pipelines, productionization, monitoring, GPU cost awareness, and model lifecycle management.
• Experience with Spark or an equivalent batch data stack, schedulers such as Airflow, and cloud training and serving such as AWS SageMaker.

Nice to have

• Familiarity with Hugging Face Transformers, PEFT, TRL, and modern inference stacks such as vLLM.
• Experience using agentic AI tools for engineering work.
• Understanding of dataset licensing and provenance for commercial use.

Culture & Benefits

• Ownership, agency, and follow-through are valued.
• Decisions emphasize impact, judgement, speed, and the broader business context.
• Continuous learning, high standards, tenacity, and open debate are encouraged.
• Career growth opportunities include taking on greater challenges.
• Competitive performance-based compensation and a candid, open, collaborative culture.