Job Description
The Future is Here, and We Need You to Build It.
At Zai Corporation, we are not just predicting the future of technology; we are architecting it. We are looking for a visionary Lead Generative AI & Machine Learning Engineer to spearhead our next-generation AI initiatives. If you are passionate about pushing the boundaries of Large Language Models (LLMs), fine-tuning, and autonomous agents, this is your opportunity to lead the 2026 standard of intelligent systems.
In this high-impact role, you will work alongside world-class researchers and engineers to deploy scalable, ethical, and cutting-edge AI solutions that redefine user experiences.
Responsibilities
- Design and architect scalable machine learning pipelines for next-gen Generative AI applications, focusing on efficiency and cost-effectiveness.
- Lead the research, development, and deployment of state-of-the-art LLMs and diffusion models tailored for enterprise use cases.
- Implement advanced fine-tuning strategies and RAG (Retrieval-Augmented Generation) architectures to enhance model accuracy and relevance.
- Collaborate with cross-functional product teams to translate complex AI capabilities into intuitive, high-conversion user interfaces.
- Establish robust MLOps practices, ensuring continuous integration, monitoring, and model governance across the entire lifecycle.
- Mentor junior engineers and data scientists, fostering a culture of innovation, technical excellence, and continuous learning.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field (or equivalent extensive practical experience).
- 5+ years of professional experience in Machine Learning, with at least 2 years in a leadership or senior engineering capacity.
- Expert proficiency in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Strong understanding of Natural Language Processing (NLP) and deep learning theory, including transformer architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Demonstrated ability to optimize models for production environments, including quantization and model serving.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.