About us: At Fujitsu AI Lab, we are at the forefront of research, innovation, and product development in areas such as AI (artificial intelligence), Machine Learning (ML), and Data Science. Our technologies are integrated into Fujitsu’s products and customer systems across sectors like finance, telecommunications, manufacturing, healthcare, and social infrastructure. We are committed to shaping the future with cutting-edge AI research, driving advancements in fields such as Generative AI, Cognitive AI, Graph ML, ML models for complex temporal and multimodal data.
Role Purpose As Research Manager/IC:
- Team Leadership : Manage a team of researchers, guiding them in developing innovative AI solutions, creating research proposals aligned with Fujitsu’s AI strategy, and collaborating with global teams and stakeholders.
- Project Execution : Lead technical execution and ensure timely delivery of research projects, support patent filings, and publication in top-tier conferences and journals.
- Hands-on Technical Work : Lead and participate in prototyping, algorithm development, and mathematical modelling to drive research.
- Business Collaboration : Work with business units to demonstrate new technologies, create use cases, and validate research outcomes.
- Mentorship and Leadership : Provide thought leadership, address technical challenges, and mentor the team to develop into future technical leaders.
- Stakeholder Engagement : Regularly update leadership on project progress and help expand research impact across business units.
- Global and Academic Collaboration : Serve as a domain expert for Global Research and build partnerships with academic institutions.
- Foster Excellence : Promote best practices to build an impactful, collaborative research team.
Technical Skills
We are seeking candidates with expertise in the following areas, along with a strong publication track record in top-tier conferences (e.g., A* conferences):
- Advanced AI Expertise:
- Deep understanding of fundamentals of AI models at theoretical and implementation level, theoretical machine learning, statistical theory of learning, AI Agents and multi-agentic systems and related technologies , and their practical implementation in various domains.
- Previous experience in research around Agentic and multi-agentic technologies such as self-adaptive, self-evolving, self-learning agents with world models, agentic memory etc is preferred. Good understanding of training algorithms, protocols and frameworks for Agentic systems is essential.
- Prior experience of testing Agents and multi-agent system in real-world environment is desirable.
- Up to date with the latest in AI algorithms & neural architectures ( e.g. , attention, transformers, state-space models, etc.) and their application to generative AI (LLMs) , explainable AI, causal reasoning, knowledge representation, concept discovery & extraction, graph AI, etc . Hands-on training of LLM (SFT, RLHF etc.) is strongly desirable.
- Desirable: An ideal researcher is one capable of performing multi-disciplinary research, rapidly learn concepts in new domain, and apply to the problem in hand, is well-informed with latest conceptual advancements in the overall AI space, and able to include them in his ideas and project at speed
- Proficient in AI/ML Programming : Advanced skills in coding, working with AI/ML libraries, and familiar with major AI platforms and tools.
Strong publication track in top-tier conferences such as ICML, ICLR, NeurIPS, AAAI, IJCAI, AISTATS, SIGKDD, ACL, NAACL etc. For computer vision domain, publications in top CV conferences like CVPR, ICCV, ECCV is expected
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Experience and additional Expectations
- 7-12 years in core research in a reputed organization in the field of Artificial Intelligence (AI) or Machine Learning with good publication track record with PhD in a relevant field.
- Experience in leading a research team/large project, some experience working across global teams, managing remote team members etc.
- Experience creating innovative ideas with potential for intellectual property (IP), market-relevant business use-cases.
- Experience working with multiple stakeholders, collaborating with teams spread across different domains, working in a multi-disciplinary setup
- Excellent communication skills, authoring research papers and presentation skills
- Adaptable, willing to learn new fields and develop new skill sets, fast learner with ability to convert ideas to practical methods or find innovative solutions to use-cases.
