Job Description

Accountabilities

• Formulate original and high-impact research questions around model architectures, efficiency, reasoning, memory and adaptation.
• Translate research ideas into rigorous experimental programs with clear hypotheses, evaluation criteria and measurable outcomes.
• Design, implement and evaluate architectural changes at meaningful model scales.
• Research efficient, sparse and adaptive attention mechanisms, long-context architectures, persistent memory and selective computation.
• Investigate new approaches to reasoning, continual adaptation and dynamic inference.
• Develop methods that preserve or improve model quality while reducing training and inference costs.
• Conduct large-scale model training and evaluation, interpreting ambiguous experimental results and drawing robust conclusions.
• Collaborate with engineering teams to turn research concepts into efficient, scalable implementations.
• Publish original research and contribute to open-source models, methodologies and research tools.
• Mentor researchers and contribute to defining priorities, methodologies and the long-term direction of the research stream.
• Communicate technical findings clearly to research and engineering stakeholders and independently lead research initiatives from concept through validation.

Requirements

• PhD in machine learning or a closely related field, or equivalent depth of research experience.
• Deep understanding of transformers, attention mechanisms, language-model training and modern neural network architectures.
• Strong track record of research publications or comparable evidence of original and impactful research.
• Proven ability to formulate research hypotheses, design rigorous experiments and extract meaningful conclusions from complex or ambiguous results.
• Strong implementation skills in Python and experience with a modern deep-learning framework.
• Hands-on experience training, evaluating or experimenting with machine-learning models at scale.
• Strong technical communication skills and the ability to independently lead research projects.
• Experience with long-context modeling, memory systems, sparse or linear attention, model distillation, distributed training or efficient inference is particularly valuable.
• Ability to collaborate effectively with engineering and research teams while maintaining ownership of individual research initiatives.
• Curiosity, intellectual rigor and a strong interest in solving challenging problems at the frontier of AI.
• Ability to work effectively in a fast-paced, international and continuously evolving research environment.

Benefits

• Competitive compensation.
• Opportunities for career growth, continuous learning and professional development.
• Flexible working environment with a high degree of ownership and autonomy.
• Collaborative and innovative culture bringing together experienced researchers, engineers and AI specialists.
• Opportunity to work on impactful, frontier AI projects with potential applications across real-world use cases.
• International environment with talented teams and opportunities for cross-functional collaboration.
• Opportunity to contribute to open-source AI models, research methods and tools.
• Meaningful opportunity to influence the development of future AI architectures and systems.
• Trust-based environment that encourages bold thinking, experimentation and continuous growth.