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Strengthening Naver’s AI Research Department: Projects, Roles, and Hiring Competencies

General Report August 7, 2025
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TABLE OF CONTENTS

  1. Overview of Naver’s AI Research Department
  2. Major Ongoing Projects and National Initiatives
  3. South Korea’s AI Ecosystem and Infrastructure Context
  4. Emerging AI Trends Shaping Research Directions
  5. Key Roles and Competencies in Naver AI Research
  6. Assessing Big Data Expertise in Recruitment
  7. Future Directions and Talent Development Strategies
  8. Conclusion

1. Summary

  • As of August 7, 2025, Naver’s AI research department stands as a critical hub for innovation and technological advancements within the organization and the broader South Korean landscape. This analysis delves into Naver's structural organization, focusing on its mission to leverage artificial intelligence (AI) to elevate existing services and spearhead new technological solutions. The report elucidates that Naver operates with a workforce of approximately 4, 700 employees, helmed by CEO Choi Soo-yeon, who has made innovation in AI and cloud technology a top priority. Naver's AI Research Department is not only pivotal in executing groundbreaking projects—including digital-twin disaster prevention and the development of national foundation models—but also plays an essential role in aligning its goals with South Korea’s national interests in advancing AI capabilities. Furthermore, the ongoing and ambitious projects within the department reflect a well-integrated approach where collaborations with academic institutions and research organizations enhance Naver's technological prowess. Naver is strategically positioned within the national R&D framework, contributing to critical cloud services that synergize with governmental objectives. A clear focus on cultivating core job roles has emerged, highlighting the necessity for talent with extensive competencies in big data, cloud architecture, and machine learning applications. Naver’s efforts to refine talent acquisition and development strategies, especially through rigorous competencies assessment for hires, underscore its commitment to nurturing a forward-looking workforce adept at navigating the evolving AI landscape.

  • The current landscape of South Korea's AI ecosystem suggests robust governmental support, evidenced by a substantial investment of $65 billion aimed at reinforcing the nation’s standing as a global AI leader. Critical benchmarks in AI adoption, particularly in Seoul, highlight the successes achieved through public-private collaborations. With a strong emphasis on the development of advanced infrastructures and regulatory frameworks, the AI ambition of South Korea is poised to foster a vibrant landscape for innovation, supporting enterprises like Naver in their quest for accelerated research and product advancement.

2. Overview of Naver’s AI Research Department

  • 2-1. Corporate profile and research mandate

  • Naver Corporation, established in 1999 and based in Seongnam, South Korea, has evolved into a leading global technology company. As of August 7, 2025, Naver has a workforce of approximately 4, 700 employees and is recognized for its contributions to various fields, particularly artificial intelligence (AI), cloud computing, and fintech. The company's comprehensive suite of services, such as its search engine, mobile applications, and online advertising solutions, enhance user experiences while meeting the dynamic needs of businesses. Naver is under the stewardship of CEO Choi Soo-yeon, who has emphasized the importance of continual innovation in AI and cloud technology, positioning the organization to remain at the forefront of tech advancements.

  • Naver's research mandate directs its focus towards leveraging AI to improve its existing services and develop new solutions. The AI Research Department is tasked with undertaking cutting-edge projects that align with the company’s strategic goals of advancing technology and expanding market reach. The department prioritizes not only technological development but also the commercialization of its research outcomes, ensuring that innovations translate into real-world applications.

  • 2-2. Organizational positioning of AI research

  • Within Naver, the AI Research Department occupies a critical role in shaping the company’s technological trajectory. It functions as a core component of the corporate structure, collaborating closely with teams across various divisions. This organizational positioning facilitates an integrated approach to research and development where insights and practices from AI are directly applied to enhance products and services.

  • The department's strategic alignment with Naver’s broader corporate goals underscores its importance in driving growth and maintaining competitive advantage in the AI landscape. By fostering an environment that encourages interdisciplinary collaboration, Naver ensures that its AI initiatives reflect a synergy of diverse perspectives and expertise, ultimately enriching the company’s offerings.

  • 2-3. Strategic partnerships and consortium roles

  • Naver actively engages in strategic partnerships and participates in consortiums that further its AI research objectives. These collaborations have proven essential in expanding the company’s technological capabilities and fostering innovation. Such partnerships provide access to complementary expertise and resources, facilitating projects that might be challenging to undertake independently.

  • As of now, Naver collaborates with various academic institutions, research organizations, and other tech companies to cultivate an ecosystem conducive to AI advancement. Through these alliances, the company also advocates for responsible AI deployment, addressing ethical considerations and societal impacts of AI technology. By being part of broader consortia, Naver also positions itself as a significant player in discussions concerning regulatory frameworks and standards within the AI domain, thus shaping the industry's future landscape.

3. Major Ongoing Projects and National Initiatives

  • 3-1. Synergies between cloud services and national R&D

  • Currently, NAVER Cloud is at the forefront of creating synergies between cloud services and national research and development (R&D) initiatives in South Korea. The ongoing Independent AI Foundation Model Project serves as a prime example of this strategy, where NAVER Cloud plans to integrate its existing AI technologies developed in previous projects with the new initiatives to enhance service efficiency and accessibility. Through its collaboration with various academic institutions and tech companies, NAVER Cloud aims to develop an 'Omni Foundation Model' that harnesses its multimodal classification capabilities, expected to elevate domestic AI technologies to a competitive global level. Furthermore, NAVER Cloud's ongoing partnerships with other organizations reflect a commitment to aligning cloud-based services with national priorities in AI development and application.

4. South Korea’s AI Ecosystem and Infrastructure Context

  • 4-1. National AI investment landscape

  • As of August 2025, South Korea has committed a substantial $65 billion in investments to establish itself as a global leader in artificial intelligence (AI). These investments are integral to enhancing the nation’s AI infrastructure and capabilities, reflecting a concerted effort by the government and private sector to drive technological advancements. The funding encompasses the development of advanced GPU infrastructures and data centers, crucial for supporting the emerging AI technologies. Notably, the National AI Computing Center, set to deploy 15, 000 advanced GPUs by 2027, underscores the government's dedication to achieving technological sovereignty and fostering an ecosystem conducive to AI innovation.

  • 4-2. City-level AI adoption benchmarks

  • Recent assessments illustrate that Seoul has achieved remarkable progress in AI adoption, ranking second globally among the 100 major cities reviewed for their AI initiatives. This ranking highlights Seoul's effective strategies in leveraging AI across various sectors, notably healthcare and education, driven by investments from both the public and private sectors. The city's comprehensive approach, exemplified by the strong cooperation between entities like SK Telecom and Naver, has positioned Seoul as a leading example of successful AI integration within urban contexts.

  • 4-3. Industry strengths and geopolitical dimensions

  • The South Korean tech ecosystem, characterized by robust partnerships between conglomerates and startups, is central to the nation’s AI ambitions. Major players, such as Samsung and SK Hynix, establish South Korea’s competence in semiconductors which is pivotal for AI infrastructure development. The geopolitical landscape is equally important, as South Korea strives to maintain strategic autonomy amidst intense global competition. Embracing the concept of 'sovereign AI', the South Korean government is taking steps to ensure that domestic AI resources and technologies can operate independently from foreign dependencies, thereby enhancing national security while promoting sustainable economic growth.

5. Emerging AI Trends Shaping Research Directions

  • 5-1. Advances in generative and general AI

  • As of August 7, 2025, generative and general AI technologies have rapidly advanced, influencing various industries significantly. Recent reports indicate that generative AI, which utilizes algorithms to create text, images, and even code based on input, is poised to add between $150 billion and $275 billion to the fashion industry’s operating profits by 2030. Moreover, the technology has begun to redefine creativity, allowing professionals in the arts and design sectors to collaborate with AI in innovative ways, thus enhancing productivity and creativity. The blending of human creativity with AI has sparked discussions on the implications for authorship, emotional design, and the very nature of artistic endeavor, prompting a reevaluation of what constitutes creativity in the digital age.

  • Furthermore, AI's ability to analyze extensive datasets fosters breakthroughs in sectors such as healthcare and finance, where predictive models are increasingly vital for decision-making. For example, AI applications can identify patterns in medical data to enhance early diagnosis and tailored treatments, while financial institutions leverage AI for improved risk assessment and fraud detection.

  • 5-2. Cross-border technology deployment

  • The significance of cross-border technology deployment is increasingly evident, particularly as geopolitical tensions prompt companies to reassess their global strategies. Organizations are pivoting towards regions with promising growth potential, such as Southeast Asia, where the demand for AI services is soaring. Chinese tech firms, for instance, have heavily invested in regional digital infrastructure, with companies like Alibaba and Tencent establishing data centers and AI solutions across various Southeast Asian nations. This strategic focus not only facilitates local operational efficiencies but also enables the rapid deployment of AI-driven services tailored to the unique needs of these markets.

  • Moreover, cross-border collaborations have been instrumental in advancing AI adoption in emerging economies, allowing for the sharing of expertise and resources between developed and developing nations. These partnerships are expected to enhance the local tech ecosystem, leading to increased innovation, workforce development, and ultimately, economic growth.

  • 5-3. 3D printing and specialized LLM applications

  • The integration of AI in 3D printing is revolutionizing manufacturing processes in Japan and beyond, as shown by the emergence of AI-driven startups focused on sustainability and efficiency. These innovations encompass everything from rapid prototyping to the creation of complex components that optimize material usage. One noteworthy example includes Japan's launch of the world’s first 3D-printed train station, showcasing the transformative potential of AI in traditional industries. AI technologies aid in predictive maintenance and quality control, streamlining production cycles and improving accuracy.

  • Additionally, large language models (LLMs) enhance 3D printing applications through improved design capabilities and user interfaces. AI systems can now automate complex design tasks and enable rapid adjustments based on real-time feedback, solidifying the alliance between advanced manufacturing and intelligent systems.

  • 5-4. Automation tools in enterprise workflows

  • As organizations accelerate digital transformation, automation tools have become pivotal in redefining enterprise workflows. AI technologies are streamlining processes across various functions, from data analysis to customer relationship management. For instance, the emergence of powerful AI assistants capable of managing spreadsheets and data analysis tasks without human input is reshaping the skill requirements for many job roles. Workers are increasingly required to shift their focus from manual data management to higher-level strategic thinking and creativity.

  • This evolution signifies a broader trend where AI acts as an enabler, allowing employees to utilize their cognitive skills effectively while letting AI undertake repetitive tasks. By automating routine functions, businesses can enhance productivity, reduce operational costs, and respond swiftly to market changes, thereby gaining a competitive edge in an increasingly dynamic business environment.

6. Key Roles and Competencies in Naver AI Research

  • 6-1. Core research and engineering positions

  • Naver's AI research department requires a diverse array of talent across core research and engineering positions. These roles include data scientists, machine learning engineers, and software developers, each possessing a unique set of skills that blend technical expertise with innovative thinking. Data scientists are primarily responsible for analyzing complex datasets to derive insights and predictions, while machine learning engineers focus on developing algorithms and models that enhance AI functionality. Software developers play a critical role in implementing these algorithms into scalable applications and systems, ensuring that the products are not only functional but also user-friendly. As highlighted in a recent report, the integration of diverse skill sets in these positions is crucial for advancing Naver's AI capabilities, especially in projects like digital-twin disaster prevention and foundation model development.

  • 6-2. Desired technical and soft skills

  • In addition to strong technical skills, candidates for Naver's AI research positions are expected to possess essential soft skills. Technical proficiency includes advanced knowledge in programming languages such as Python and R, familiarity with machine learning frameworks like TensorFlow or PyTorch, and experience with data engineering tools. Beyond technical capabilities, soft skills such as teamwork, problem-solving, and adaptability are highly valued. The fast-evolving AI landscape necessitates professionals who can work collaboratively across disciplines and contribute to multidisciplinary projects, fostering innovation and enhancing project outcomes. A recent discussion with Arfi Siddik Mollashaik emphasized the importance of communication skills, especially in contexts where complex AI systems intersect with regulatory environments, underscoring the need for professionals who can articulate technical concepts clearly to non-technical stakeholders.

  • 6-3. Alignment with industry demands

  • The AI landscape is continuously evolving, marked by rapid advancements in technology and changing market needs. To remain competitive, Naver's AI research department emphasizes the alignment of its core competencies with prevailing industry demands. Professionals are expected to stay informed about trends such as generative AI, natural language processing, and AI-driven automation. As indicated in the latest analysis of the AI market, there is a significant push for secure and compliant AI solutions across various sectors, including finance, healthcare, and retail. This shift demands that Naver's talent pool not only adapt to new technologies but also leverage them in ways that meet regulatory requirements and user expectations. Consequently, continual learning and professional development become vital components of the workforce strategy.

  • 6-4. Data security and privacy expertise

  • Given the mounting concerns surrounding data security and privacy in AI applications, expertise in these areas is increasingly crucial for Naver's AI research team. Candidates are expected to understand privacy regulations such as GDPR and CCPA, and possess skills to design and implement robust data classification frameworks. Arfi Siddik Mollashaik's insights into the need for AI-driven data security solutions highlight the urgency for professionals who can navigate complex security landscapes while integrating AI capabilities to enhance data privacy. Successful data security professionals within Naver will not only focus on protecting data but also innovate solutions that balance security with user privacy. The emphasis on ethical AI practices further reinforces the importance of hiring talent with a strong understanding of regulatory compliance and ethical standards in data handling.

7. Assessing Big Data Expertise in Recruitment

  • 7-1. Technical assessments for a 960-hour big data curriculum

  • As the demand for skilled professionals in big data intensifies, organizations are increasingly relying on structured recruitment processes that include technical assessments to evaluate potential candidates. A comprehensive 960-hour big data curriculum has emerged in South Korea, designed to equip candidates with essential competencies in data analysis, machine learning, and data visualization. This curriculum focuses not only on theoretical knowledge but also on practical applications of big data technologies, ensuring that graduates can meet industry demands upon entering the workforce.

  • These technical assessments typically involve hands-on coding tests, data manipulation tasks, and the use of big data tools such as Apache Hadoop and Spark. Candidates are evaluated on their ability to manage data pipelines, perform exploratory data analysis, and deploy machine learning models, which are critical skills in today’s data-driven environments.

  • 7-2. Case-based evaluations and scenario tests

  • To further assess candidates' practical skills, case-based evaluations and scenario tests play a pivotal role in the recruitment process. These tests simulate real-world problems that organizations encounter, challenging candidates to apply their knowledge in a controlled setting. For instance, applicants might be presented with a dataset and asked to devise a strategy for cleaning, analyzing, and extracting meaningful insights while adhering to best data security practices.

  • Such scenario tests not only gauge technical abilities but also reflect candidates' problem-solving capabilities, teamwork dynamics, and adaptability to changing conditions. This method of assessment is particularly beneficial in fields like data security, where the ability to respond to data breaches and compliance issues in real-time is crucial.

  • 7-3. Metrics for data quality and security validation

  • In the context of big data recruitment, validating candidates’ understanding of data quality metrics and security measures is essential. As organizations grapple with increasing volumes of sensitive data, the competence to assess data quality through metrics such as accuracy, completeness, consistency, and timeliness emerges as a vital skill.

  • Furthermore, candidates must demonstrate familiarity with data security frameworks that govern how sensitive information is handled. For example, understanding regulations like GDPR, PIPA, and compliance standards is paramount in ensuring that the deployment of big data solutions does not compromise user privacy. Evaluating candidates against these metrics allows organizations to ensure that new hires not only possess technical expertise but also a holistic understanding of data governance.

  • 7-4. Behavioral interviews and project portfolios

  • Behavioral interviews serve as a valuable adjunct to technical assessments, as they allow employers to evaluate candidates' soft skills and cultural fit within the organization. In these interviews, candidates may be asked to describe past experiences relating to conflict resolution, teamwork, and time management—skills that are crucial in collaborative environments where big data initiatives often require interdisciplinary efforts.

  • Alongside behavioral interviews, reviewing project portfolios gives recruiters insight into a candidate's practical experience with big data projects, providing evidence of their competencies in handling various tools and platforms. Portfolios that showcase a variety of projects, especially those that incorporate effective coding practices, innovative data solutions, and compliance with data security principles, can significantly enhance a candidate's profile.

8. Future Directions and Talent Development Strategies

  • 8-1. Career path frameworks in AI research

  • As organizations increasingly integrate AI into their operations, the establishment of clear career path frameworks becomes critical. Future frameworks for AI research roles will likely need to blend traditional skills with new, tech-focused competencies. Integrating AI into existing jobs will require a clear understanding of how these roles evolve, necessitating that professionals adapt to more strategic functions within organizations. In particular, positions will need to pivot from routine tasks towards greater emphasis on collaboration, design thinking, and problem-solving aspects. This shift highlights the importance of developing robust mentoring and support systems that guide employees through transitions in their roles as AI continues to shape the landscape.

  • 8-2. Continuous learning via hyper-personalized platforms

  • The trend towards hyper-personalized learning platforms is set to revolutionize continuous education within AI and related fields. These platforms use generative AI to tailor training materials and experiences to individual learner’s needs, preferences, and progress. By analyzing learning patterns, tools can deliver content that not only matches skill levels but also anticipates learning needs, thereby driving engagement and retention. Organizations will increasingly adopt such platforms to upskill their workforce, ensuring that employees remain competitive in an ever-evolving landscape where AI technology continues to be a pivotal element.

  • 8-3. Workplace adaptation to AI-driven roles

  • The shift to AI-driven tools and roles will necessitate systematic adaptations within workplace environments. Organizations are likely to implement hybrid models that combine AI capabilities with traditional human roles, fostering an ecosystem where employees leverage AI for increased productivity. As new use cases for AI emerge—particularly in complex decision-making and automated processes—employees will require training not only in technical skills but also in navigating these AI-enhanced environments. Equipping teams with resources and knowledge for effectively working alongside AI will be essential for maximizing the benefits of integration, ultimately leading to more efficient task completion and innovative solutions.

  • 8-4. Long-term workforce planning

  • Effective long-term workforce planning will become increasingly essential as organizations look to harness the benefits of AI technologies while managing the implications on job structures. This planning will involve strategic foresight into skill requirements, hiring practices, and employee development based on predictions about future industry needs and technological advancements. Companies will need to assess their workforce capabilities regularly, adapting recruitment and training programs to fill emerging gaps in expertise, particularly in areas such as AI ethics, data privacy, and security. Additionally, roles that emphasize human judgment, creativity, and emotional intelligence will remain crucial, highlighting the importance of a balanced approach to workforce development that includes both technical and soft skills.

Conclusion

  • In conclusion, Naver’s AI research department is uniquely positioned at the nexus of corporate innovation and national technology initiatives, effectively leveraging interdisciplinary collaboration to address complex challenges. As the organization continues to advance projects related to digital-twin disaster prevention and foundational model development, the multidimensional team approach encompassing cloud architecture, data security, and advanced machine learning will be crucial. To attract and retain exceptional talent, Naver must prioritize robust technical assessments that accurately reflect both theoretical and practical big-data skills, employing scenario-based interviews to evaluate real-world problem-solving abilities. Looking ahead, the integration of hyper-personalized learning pathways and well-defined career trajectories will be paramount in fostering sustained research excellence. By focusing on continuous learning and adopting a proactive stance toward workforce development, Naver can ensure its strategic growth in an AI landscape characterized by rapid change and innovation. Moreover, as the competitive dynamics in AI evolve, Naver has the opportunity to shape its trajectory, contributing significantly to the national agenda and reinforcing its status as a leader in AI research and application.

  • The insights derived from this analysis underscore the importance of adaptability and foresight in workforce planning, especially as organizations navigate the complexities introduced by AI technologies. Through thoughtful recruitment strategies and a commitment to ethical AI practices, Naver can continue to play a pivotal role in shaping the future of AI, not only within South Korea but on a global scale.

Glossary

  • Naver Corporation: Founded in 1999 and headquartered in Seongnam, South Korea, Naver Corporation is a leading global technology company, particularly recognized for its advancements in AI and cloud computing. As of August 7, 2025, Naver employs approximately 4, 700 individuals and emphasizes innovation under the leadership of CEO Choi Soo-yeon.
  • AI Research Department: Naver's AI Research Department focuses on leveraging artificial intelligence to enhance services and develop new technological solutions. It plays a crucial role in projects such as digital-twin disaster prevention and national foundation models, ensuring that innovations align with both corporate and national objectives.
  • Foundation Models: Foundation models are AI systems that use large-scale datasets to establish general intelligence capabilities, from which more specialized models can be derived. As of 2025, Naver's AI department is developing national foundation models to enhance AI capabilities across various sectors in South Korea.
  • Big Data: Big Data refers to the vast volumes of data generated from various sources, which demands advanced processing techniques for analysis. Naver emphasizes competencies in big data within its AI Research Department, reflecting the growing demand for professionals skilled in extracting insights from large datasets.
  • Cloud Services: Cloud services provide on-demand computing resources and storage via the internet, facilitating flexibility and scalability for businesses. Naver Cloud, positioned as a leader in this space, aims to integrate AI technologies into its offerings to bolster efficiency and national R&D initiatives as of 2025.
  • Disaster Prevention: Disaster prevention is an initiative that employs technology to forecast and mitigate potential disasters. Naver's ongoing projects include developing AI-driven digital twins for disaster prevention, enhancing the ability to simulate and respond to emergencies effectively.
  • Hyper-Personalization: Hyper-Personalization leverages AI and data analytics to create highly individualized user experiences. In the context of Naver's AI initiatives, it emphasizes tailoring services to meet specific user preferences, particularly within the framework of continuous learning and talent development strategies.
  • GPU (Graphics Processing Unit): GPUs are specialized processors that accelerate data processing and are particularly crucial for handling AI workloads. South Korea's government plans to deploy 15, 000 advanced GPUs by 2027 to solidify its position in AI development through enhanced computational capabilities.
  • Data Security: Data security involves protecting digital information from unauthorized access, corruption, or theft. Naver's AI Research Department prioritizes data security expertise, reflecting the urgency of managing privacy regulations like GDPR in AI applications, which are critical in today's data-centric environment.
  • Competencies: Competencies refer to the combination of skills, knowledge, and abilities that a candidate must possess to perform effectively in a role. Naver outlines key competencies for its AI research positions, emphasizing the importance of both technical skills and soft skills such as adaptability and communication.
  • Generative AI: Generative AI is a type of artificial intelligence that creates new content, such as text, images, or music, based on training data. As of 2025, it is anticipated to significantly impact various industries, including the potential to add substantial profits to sectors like fashion through innovative applications.
  • Framework: In the context of AI and technology, a framework is a structured approach to solving problems by outlining the tools, methodologies, and best practices that should be followed. Future career path frameworks in AI research will guide professionals as roles evolve to include more strategic and collaborative responsibilities.
  • Scenario Tests: Scenario tests simulate real-world challenges that candidates may face in their roles, providing insights into their problem-solving abilities and adaptability. This assessment method is particularly crucial in hiring for data security and AI positions, reflecting current industry needs as of August 2025.

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