Job Description
We are on a mission to define the technological landscape of 2026 and beyond. As a Senior Generative AI Engineer at Zai Future Labs, you will be at the forefront of building scalable, secure, and innovative Large Language Models (LLMs) and autonomous agents. We are looking for a visionary engineer to bridge the gap between theoretical AI research and production-grade applications that solve complex real-world problems.
Why join us?
- Work on cutting-edge projects that will shape the future of human-AI interaction.
- Competitive compensation and equity packages.
- Top-tier engineering team and access to the latest hardware for training.
Key Responsibilities:
- Design, train, and fine-tune large-scale foundation models using state-of-the-art architectures.
- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Optimize inference latency and throughput for high-volume deployment environments.
- Collaborate with product and research teams to define AI product requirements and roadmaps.
- Ensure robustness, security, and ethical AI practices in all deployed models.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications:
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related field (or equivalent experience).
- 5+ years of experience in software engineering, with a focus on machine learning/deep learning.
- Proficiency in Python, PyTorch, or TensorFlow.
- Strong understanding of transformer architectures and NLP techniques.
- Experience with vector databases (Pinecone, Milvus) and orchestration tools (Kubernetes, Docker).
- Experience deploying models via API endpoints (FastAPI, Flask) and serving infrastructure (vLLM, TGI).
- Excellent problem-solving skills and ability to work in a fast-paced, agile environment.
Responsibilities
- Design, train, and fine-tune large-scale foundation models using state-of-the-art architectures.
- Implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Optimize inference latency and throughput for high-volume deployment environments.
- Collaborate with product and research teams to define AI product requirements and roadmaps.
- Ensure robustness, security, and ethical AI practices in all deployed models.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related field (or equivalent experience).
- 5+ years of experience in software engineering, with a focus on machine learning/deep learning.
- Proficiency in Python, PyTorch, or TensorFlow.
- Strong understanding of transformer architectures and NLP techniques.
- Experience with vector databases (Pinecone, Milvus) and orchestration tools (Kubernetes, Docker).
- Experience deploying models via API endpoints (FastAPI, Flask) and serving infrastructure (vLLM, TGI).
- Excellent problem-solving skills and ability to work in a fast-paced, agile environment.