We are looking for a Senior ML Engineer 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 new ideas.  

• Rigorous experimentation to take the final solution to product, submit IP and publish at top-tier conferences.  

• Stay at the forefront of rapidly evolving research in foundation models and agentic AI, identifying opportunities where fundamental advances can create differentiated product capabilities. 

Leadership & Collaboration 

• Lead technical design reviews, write engineering RFCs, and set quality standards for the team. 

• Mentor junior and mid-level ML engineers through code reviews, 1:1s, and pair-programming sessions. 

• Collaborate with product, research, and infrastructure teams to translate research ideas into shipped features. 

Required Qualifications 

• 9+ years of hands-on ML engineering experience in industry or research. 

• MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field, with significant industry or research experience.  

• Strong fundamentals in machine learning, deep learning, optimization, and statistical modeling.  

• Demonstrated research experience in one or more of:  

• Large Language Models and NLP  

• Generative modeling (diffusion, flow matching, autoregressive models, VAEs, etc.)  

• Computer Vision  

• Multimodal foundation models  

• Representation learning  

• Reinforcement learning / learning from interaction  

• Reasoning and planning  

• Demonstrated ability to formulate novel research problems, develop new approaches, and experimentally validate hypotheses.  

• Strong publication record and/or demonstrated track record of delivering novel ML research with measurable impact.  

• Strong programming and experimentation skills, particularly with modern deep learning frameworks 

• Excellent written and verbal communication skills with cross-functional stakeholders.