Summary
Origen is hiring an AI Scientist for its AI Lab in Abu Dhabi. The role conducts original research on foundation models, large language models, multimodal systems, model architectures, training methods, alignment, safety, and robustness. Responsibilities include leading large-scale distributed training experiments, publishing research, mentoring junior researchers, and helping move research prototypes into production systems. Applicants should have a PhD in a relevant quantitative field, a strong research publication record, and experience with frameworks such as PyTorch or JAX.
The posting
As published on origen.ae. Pulled from the employer's site and shown as they wrote it.
Position Overview
As an AI Scientist at Origen AI Lab, you will conduct original, high-impact research in large-scale machine learning and foundation models. This role is designed for individuals who are capable of advancing the state-of-the-art in artificial intelligence through rigorous experimentation, theoretical insight, and scalable system implementation.
You will work at the intersection of research and engineering—developing novel model architectures, improving large-scale training methodologies, and exploring alignment, robustness, and efficiency challenges in next-generation AI systems.
The ideal candidate demonstrates scientific independence, deep technical mastery, and the ability to transform cutting-edge ideas into reproducible research outcomes and production-ready systems.
This position offers the opportunity to shape the long-term AI research direction of Origen and contribute to foundational AI capabilities deployed in mission-critical and large-scale environments.
Key Responsibilities
1. Frontier Research & Innovation
• Conduct original research in foundation models, including large language models (LLMs), multimodal systems, and generative architectures.
• Propose and validate novel model designs, optimization strategies, and scaling techniques.
• Advance research in areas such as self-supervised learning, reinforcement learning, model alignment, safety, and robustness.
• Design new evaluation metrics and benchmarking methodologies.
2. Large-Scale Model Development
• Lead end-to-end large-scale training experiments on distributed GPU/accelerator clusters.
• Optimize model training efficiency, memory utilization, and inference performance.
• Explore model compression, quantization, and edge deployment strategies.
• Ensure reproducibility, experimental rigor, and scientific documentation.
3. Research Publication & Technical Leadership
• Publish research in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL).
• Contribute to patents and high-impact technical white papers.
• Present research internally and externally to stakeholders, partners, and academic collaborators.
• Mentor junior researchers and contribute to building a high-caliber research culture.
4. Research-to-Production Transition
• Collaborate with engineering and solution teams to translate research prototypes into scalable AI systems.
• Provide architectural guidance for production-grade AI deployment.
• Evaluate trade-offs between theoretical performance and real-world constraints.
Candidate Profile
1. Education & Research Background
• PhD in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or related quantitative field.
• Strong publication record in leading AI conferences or journals.
• Demonstrated ability to independently lead research projects.
2. Technical Expertise
• Deep understanding of transformer architectures, large-scale optimization, and deep learning theory.
• Hands-on experience training large-scale models in distributed environments.
• Proficiency in PyTorch, JAX, or equivalent ML frameworks.
• Strong mathematical foundation in probability, linear algebra, and optimization.
3. Research Excellence Indicators
• Track record of novel contributions to model architectures, training techniques, or AI evaluation frameworks.
• Experience working with large datasets and high-performance compute environments.
• Ability to formulate research problems from first principles.
• Strong experimental design and analytical reasoning skills.
4. Personal Attributes
• Scientific rigor and intellectual curiosity.
• High standards for research quality and reproducibility.
• Ability to operate in ambiguous, frontier research environments.
• Strong communication skills in English; cross-cultural collaboration experience preferred.