Research Scientist (Generative & Agentic AI)
Salary not listed • Taipei, Taiwan • Full-time
Posted 3mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Taipei, Taiwan
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Agent Engineer
About the job
Appier is seeking a Research Scientist to work at the forefront of generative and agentic AI. This role involves advancing Large Language Models (LLMs), Vision-Language Models (VLMs), and AI agents capable of reasoning, planning, and tool utilization for real-world problem-solving. Your research will encompass post-training techniques, reasoning, multimodal intelligence, and autonomous agentic systems. You will play a key role in shaping Appier's core AI capabilities, contributing to publications at top AI/ML conferences, and collaborating with scientists and engineers to translate cutting-edge research into impactful products.
Responsibilities
- Research and build agentic AI systems, including reasoning, planning, tool use, memory, and multi-agent collaboration, powered by LLMs and VLMs.
- Advance post-training techniques such as SFT, RLHF, RL with verifiable rewards, and preference optimization to enhance model capability, alignment, and reliability.
- Improve the performance, efficiency, and scalability of foundation models across training, inference, and test-time compute.
- Design rigorous evaluations and benchmarks for models and agents in real-world scenarios.
- Collaborate with cross-functional teams to integrate research findings into production applications.
- Stay abreast of frontier research, propose new research directions, and publish key findings at leading AI/ML venues.
Requirements
- Master's degree or Ph.D. in Computer Science, Electrical Engineering, Mathematics, or a related field, with research experience in AI/ML.
- Deep understanding of modern foundation models, with expertise in LLMs, VLMs/multimodal models, RL, or agentic systems.
- Hands-on experience building with LLMs, including fine-tuning, RAG, agent frameworks (tool use, function calling), or product prototyping.
- Proficiency in Python and PyTorch for building, training, and optimizing models.
- Strong ability to analyze model behavior, diagnose bottlenecks, and improve training and inference pipelines.
- Clear communication skills and a collaborative, team-first attitude.
- Publications in top AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP) are preferred.
- Experience with large-scale distributed training or LLM post-training pipelines is preferred.
- Contributions to open-source projects (e.g., agent frameworks, model or benchmark releases) are preferred.
- Passion for advancing generative and agentic AI and bridging research with real-world applications is preferred.