Taiwan AI Labs is a dynamic startup environment offering exceptional career development opportunities. We invite talented professionals to join us in shaping the future of artificial intelligence (AI) experiences. We are seeking results-driven, highly organized individuals with strong leadership skills to plan, execute, and manage various engineering projects. At Taiwan AI Labs, our mission is clear: to define the future of AI experiences.
We are developing LLM-powered agent systems that interact with tools, retrieve information, and perform multi-step reasoning in real-world scenarios.
We are looking for a Senior LLM Engineer who can not only build such systems, but also improve model behavior through techniques such as supervised fine-tuning, evaluation, and reinforcement learning to achieve reliable performance in complex tasks. This role involves owning system-level decisions and driving improvements from experimentation to production.
【Responsibilities】
1.LLM Behavior & Reasoning
- Design and improve LLM-based behavior for complex tasks through strategies such as task decomposition, context management, tool use, and effective integration of external knowledge.
2.Model Optimization
- Experience in model adaptation techniques, such as fine-tuning, preference optimization, or related methods (e.g., reinforcement learning)
- Make informed trade-offs between prompting, retrieval, data curation, and model adaptation.
3.Evaluation & Iteration
- Design and implement evaluation methods for LLM and agent performance, including task success, reasoning quality, and robustness.
- Build evaluation datasets and benchmarks aligned with real-world scenarios.
- Use evaluation results and error analysis to guide improvements across model, data, and system design.
4.System Collaboration
- Work with backend and platform engineers to deploy and iterate on LLM systems.
- Ensure solutions are practical in terms of latency, cost, and reliability.
- Contribute to API-based workflows and deployment processes when needed.
【Essential Qualifications】
- Experience building and improving LLM applications, such as RAG, agents, or similar systems.
- Solid foundation in machine learning and deep learning, with understanding of modern LLM techniques.
- Hands-on experience training or adapting LLMs, including dataset design, fine-tuning, or preference optimization.
- Ability to design model improvement strategies and make informed trade-offs between prompting, retrieval, data curation, and model adaptation.
- Experience designing evaluation strategies or frameworks for LLM or NLP systems.
- Proficiency in Python and modern ML tooling.
- Ability to translate research ideas into practical improvements that can be validated and deployed in production.
- Strong ownership in driving ambiguous problems end-to-end, with the ability to collaborate across ML, backend, and product teams and communicate technical trade-offs clearly.
【Preferred Qualifications】
- Experience applying reinforcement learning or preference optimization to improve LLM behavior
- Experience working with production ML systems, without necessarily owning infrastructure.
- Working knowledge of Docker, Kubernetes, APIs, or cloud environments in production ML workflows.
【Why Join Us】
- Work on real-world AI systems with complex reasoning and decision-making challenges.
- Improve model behavior through data, evaluation, learning, and applied system design.
- Drive end-to-end iteration from experimentation to production impact.
