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.

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.

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.