AI Applied Scientist

About the Role

We're seeking an inventive AI Applied Scientist to us and drive breakthroughs in agentic AI architecture. You will conduct cutting-edge research, design novel algorithms, and help translate state-of-the-art theory into practical solutions. Your work will shape the future of autonomous agents, creative reasoning, and predictive intelligence.

This is a hybrid role between research and applied developmentworking with the best minds in AI and contributing meaningful innovation with real-world impact.

Key Responsibilities

End-to-end ownership of products / features: Experience of delivering product features / products

Manage a small pod for product / feature delivery: Manage a team of 4-6 people to deliver the required features / products

Mentorship: Guide and support AI engineers, elevating team expertise and scientific rigor.

Cross-functional Collaboration: Work with engineers, data teams, and evaluation partners to integrate research outputs into evaluation pipelines and prototypes.

Research & Architect Novel AI Systems: Collaborate with the G42 Technical team to conceive and implement advanced agentic AI architectures.

Algorithm Development: Build new algorithms for reasoning, planning, causal inference, and trend forecasting anchored in the latest scientific advances.

Innovate and Ideate: Drive ideation and design of new models, expanding the frontier of AI capabilities.

Publish and Present: Contribute high-impact research outcomes to top-tier academic venues and technical outlets.

Qualifications

Academic Credentials: Master's or Ph.D. in AI/ML, Computer Science, or a closely related field.

Experience Level: 6 years+ (negotiable if the candidate is good and experience is less than 6 years) of relevant work experience, ideally at the intersection of research and applied AI development.

Technical Expertise:

• Strong foundation in LLMs and Multimodal models, and techniques such as reasoning, planning, causal inference, forecasting.
• Proficiency in programming languages and frameworks commonly used in AI research (e.g. Python, PyTorch).
• Experience in designing and evaluating AI models and algorithms, with a research-driven mindset.
• Scholarly Output: Record or strong potential for publishing in high-impact conferences or journals.

Soft Skills:

• Excellent problem-solving and critical-thinking abilities.
• Effective communication skills, capable of both mentoring and translating complex concepts for diverse audiences.