We are looking for a Senior Applied Scientist to lead foundational research in agentic AI and intelligent systems. This is a research-intensive role focused on advancing the underlying models, learning paradigms, and capabilities that enable AI agents to reason, plan, learn, remember, interact with tools, and operate effectively over complex, long-horizon tasks.
This is not a prompt-engineering or agent orchestration role. We are looking for a scientist with strong fundamentals in machine learning and deep learning who can develop novel modeling and learning approaches, rather than primarily assembling existing LLMs, prompts, or agent frameworks.
The ideal candidate will have deep expertise in one or more areas of generative modeling, large language models, NLP, computer vision, multimodal learning, representation learning, reinforcement learning, or related areas, along with the ability to connect these foundations to emerging agentic systems.
The role will involve identifying fundamental research problems in agentic intelligence, developing new algorithms and model architectures,
designing rigorous experiments, and translating research advances into product.
Key Responsibilities
Conduct fundamental and applied research in agentic AI, foundation models, reasoning, planning, memory, tool use, multimodal intelligence, and long-horizon interaction.
Develop novel model architectures, learning algorithms, training methodologies, and inference techniques to improve the capabilities of AI agents.
Advance the underlying intelligence of agents through research in LLMs, generative models, NLP, computer vision, multimodal learning, representation learning, and reinforcement learning.
Investigate problems such as reasoning, planning, memory, grounding, adaptation, self-improvement, tool use, and learning from interaction.
Build and evaluate research prototypes and establish rigorous experimental methodologies to validate current ideas.
Rigorous experimentation to take the final so
📌 Applied Scientist 5 (Noida)
🏢 Adobe
📍 Noida