Research Scientist (Generative & Agentic AI)

As a Research Scientist, you will work at the frontier of generative and agentic AI: advancing Large Language Models (LLMs), Vision-Language Models (VLMs), and AI agents that reason, plan, and use tools to solve real-world problems. Your research will span post-training (SFT, RLHF/RLVR), reasoning and test-time scaling, multimodal intelligence, and autonomous agentic systems. You will shape Appier's core AI capabilities, publish at top AI/ML conferences, and collaborate with scientists and engineers to turn frontier research into product impact.

Responsibilities

Research and build agentic AI systems: reasoning, planning, tool use, memory, and multi-agent collaboration, powered by LLMs and VLMs.; Advance post-training techniques (SFT, RLHF, RL with verifiable rewards, preference optimization) to improve 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 ship research into production applications.; Track frontier research, propose new 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 at least one of: LLMs, VLMs/multimodal models, RL, or agentic systems.; Hands-on experience building with LLMs: fine-tuning, RAG, agent frameworks (e.g., tool use, function calling), or product prototyping.; Proficient in Python and PyTorch; able to build, train, and optimize models effectively.; Strong ability to analyze model behavior, diagnose bottlenecks, and improve training and inference pipelines.; Clear communication skills and a team-first attitude in a fast-paced, collaborative environment.

Nice to have

Fluency with AI-assisted coding workflows is a plus; Publications in top AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP); Experience with large-scale distributed training or LLM post-training pipelines; Contributions to open-source projects (e.g., agent frameworks, model or benchmark releases); Passion for pushing the frontier of generative and agentic AI and bridging research with impactful real-world applications.