Senior Research Scientist (Architectures Research) in Râmnicu Vâlcea, Brașov at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Research Scientist (Architectures Research) based in Romania.

As a Senior Research Scientist, you will conduct frontier research focused on developing more capable, efficient and adaptable AI model architectures. You will investigate how models can attend, remember, reason and adapt more effectively while reducing the computational cost of training and inference. The role combines fundamental research with practical experimentation at meaningful model scales. You will formulate original research questions, design rigorous experiments and translate promising ideas into working implementations. You will collaborate closely with engineering teams and contribute to open-source models, methods and tools. As a senior member of the research team, you will also mentor researchers and help shape the broader research direction. This is an opportunity to work in a fast-moving, international AI environment where your research can directly influence the next generation of AI systems.

• 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.

• 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.

• 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.