Contribute to model development and fine-tuning workflows for generative AI features.
Design and evaluate retrieval strategies for grounding large models in product-relevant data.
Prototype and benchmark multi-round agentic solutions for conversational knowledge-intensive tasks.
Partner with data and platform engineers to ensure scalable deployment and monitoring.
Stay current with advancements in large language modeling, vector search, RAG, and LLM Agents.
Participate in architecture discussions with engineers and partner teams.
Collaborate with product and business stakeholders to incorporate customer experience requirements into AI solutions.
Support localization and rollout of AI features across multiple markets.
Minimum Qualifications
5+ years of hands-on experience in ML, backend engineering, data engineering
1-2 years of hands-on experience in training, fine-tuning, or evaluating LLMs
Foundational understanding of RAG architectures and vector-based retrieval systems
Exposure to multi-agent orchestration frameworks in Rust and Python.
Familiarity with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Experience with data preprocessing, tokenization, and pipeline automation.
Proficiency in machine learning libraries (transformers, datasets).
Strong problem-solving and collaboration skills, with the ability to learn quickly and adapt to production-grade systems.
Experience working with Multi-modal LLMs to enable Voice capabilities is a plus or prior experience with STT, TTS systems.
Experience with deploying to cloud environments (AWS, GCP, on-remote hybrid) is required.
Experience in training, fine-tuning, or evaluating LLMs in Chinese for Greater China market
Fluency in written and spoken Chinese to support the Greater China market
Preferred Qualifications
Strong experience partnering with business and engineering team to deliver AI solutions.
Bachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience.