Jun Kim
CEO @ Kakao Ventures
Seoul, South Korea
Jun Kim is a Series A investor at Kakao Ventures in Seoul focused on Software and Machine Learning. Jun concentrates on giving early stage support to consumer, internet, game and tech startups at Kakao Ventures. Prior to Kakao Ventures, Jun was a Co-Founder & Executive at Idea Works, a mobile service startup; an Associate at VirtualTek, an IT service and groupware startup that became a publicly listed company; a Manager at SK Communications, the provider of social networking service CyWorld/NateOn; and a Manager at CJ Holdings. His diverse experience includes business strategy execution and new business development. With his many years of experience as an engineer, Jun is well-versed in the rapidly evolving technology of the startup world. He is able to be an insightful discussion partner to startups on not only on business and management issues, but also on technical issues. Jun earned his B.S. in Nuclear Engineering from Seoul National University and his Master’s Degree in Information Systems from the Yonsei University Graduate School of Information.
- Total investments:
- 29
- Last investment:
- Lead investor:
- 28%
Last updated:
For AI agents: llms.txt
Industries Jun Kim invests in
Stages Jun Kim invests in
Countries Jun Kim invests in
Contacts
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| Personal email | ||
| Work phone | ||
| Personal phone | ||
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Investment Focus
Industry
- Software 4 (14%)
- Machine Learning 4 (14%)
- Artificial Intelligence 4 (14%)
- Internet 2 (7%)
- Health Care 2 (7%)
Stage
- Series A 7 (24%)
- Seed 6 (21%)
- Series B 3 (10%)
- Series C 3 (10%)
- Funding Round 2 (7%)
Investments
| Company | Description | Round | Links |
|---|---|---|---|
| OQT is focusing on developing QPUs utilizing neutral atoms. | $10.4M / Seed / Oct 15, 2026 | ||
| OQT is focusing on developing QPUs utilizing neutral atoms. | $2.1M / Seed / Feb 13, 2025 | ||
| Baum provides electronic design automation (EDA) software and solutions that enable engineering groups in the automotive, internet of things (IoT), mobile, networking and server markets to fully optimize the energy efficiency of their semiconductor designs. Founded in 2016 by seasoned semiconductor professionals with technical, R&D and businessdevelopment expertise, Baum is privately held and funded. | $3.8M / Series A / Dec 16, 2022 | ||
| Datumo got your back!Obtaining the proper data has always been the top issue for many AI labs/companies. To solve this problem, Datumo provides a platform that connects your company to the crowd-workers.It's MORE than just a connection! We passionately research crowd-workers and your project to develop cutting-edge technologies forthe optimized mobile crowd-sourcing.Keep disrupting the world! Just for your project, we are here to maintain the quality, cost-efficiency, or any other burdens related to the data. Datumo was founded in 2018. | $6.9M / Series A / Aug 01, 2022 | ||
| AI chip architects are often inattentive to advanced deep learning features and requirements because they believe software stacks between their silicon and algorithms can provide effective abstract layers. We disagree. We do not think such naively designed chips can efficiently utilize expensive custom silicon resources. The more native hardwaresupports are enabled, the higher performance is achieved.Rebellions Inc is building genuine AI accelerators by bi-directionally bridging the gap between underlying silicon architectures and deep learning algorithms. We push algorithm boundaries to better exploit silicon budgets, while re-architecting AI processors to incorporate sophisticated deep learning features through silicon-dedicated DL kernels.Our goal is to deliver a domain-specific AI processor along with its optimized software. This approach, together with our unique decentralized programming model, will enable customers to arm with the most powerful yet energy-efficient AI hardware as well as seamless integration. | $46.1M / Series A / Jun 03, 2022 | ||
| AI chip architects are often inattentive to advanced deep learning features and requirements because they believe software stacks between their silicon and algorithms can provide effective abstract layers. We disagree. We do not think such naively designed chips can efficiently utilize expensive custom silicon resources. The more native hardwaresupports are enabled, the higher performance is achieved.Rebellions Inc is building genuine AI accelerators by bi-directionally bridging the gap between underlying silicon architectures and deep learning algorithms. We push algorithm boundaries to better exploit silicon budgets, while re-architecting AI processors to incorporate sophisticated deep learning features through silicon-dedicated DL kernels.Our goal is to deliver a domain-specific AI processor along with its optimized software. This approach, together with our unique decentralized programming model, will enable customers to arm with the most powerful yet energy-efficient AI hardware as well as seamless integration. | $70M / Series A / Jun 02, 2022 | ||
| Lunit, abbreviated from “learning unit,” is a venture company devoted to developing advanced software for medical data analysis and interpretation via cutting-edge deep learning technology. The company's main area of expertise lies in processing medical imaging data. It envisions a near future when its systems will greatly help physicians makeaccurate, consistent, and efficient clinical decisions, not limited to diagnoses, through our data-driven imaging biomarker technology. The high level of our deep learning technology has been well demonstrated, ranked top 5 at ImageNet Challenge 2015, which is the world’s largest and most prestigious image recognition competition. What distinguishes our technology from that of other similar parties is the full accommodation of weakly labeled data, challenging the boundaries of human understanding in terms of pattern recognition. | $61M / Venture - Series Unknown / Nov 29, 2021 | ||
| AI chip architects are often inattentive to advanced deep learning features and requirements because they believe software stacks between their silicon and algorithms can provide effective abstract layers. We disagree. We do not think such naively designed chips can efficiently utilize expensive custom silicon resources. The more native hardwaresupports are enabled, the higher performance is achieved.Rebellions Inc is building genuine AI accelerators by bi-directionally bridging the gap between underlying silicon architectures and deep learning algorithms. We push algorithm boundaries to better exploit silicon budgets, while re-architecting AI processors to incorporate sophisticated deep learning features through silicon-dedicated DL kernels.Our goal is to deliver a domain-specific AI processor along with its optimized software. This approach, together with our unique decentralized programming model, will enable customers to arm with the most powerful yet energy-efficient AI hardware as well as seamless integration. | $10.8M / Private Equity / Jul 21, 2021 | ||
| AI chip architects are often inattentive to advanced deep learning features and requirements because they believe software stacks between their silicon and algorithms can provide effective abstract layers. We disagree. We do not think such naively designed chips can efficiently utilize expensive custom silicon resources. The more native hardwaresupports are enabled, the higher performance is achieved.Rebellions Inc is building genuine AI accelerators by bi-directionally bridging the gap between underlying silicon architectures and deep learning algorithms. We push algorithm boundaries to better exploit silicon budgets, while re-architecting AI processors to incorporate sophisticated deep learning features through silicon-dedicated DL kernels.Our goal is to deliver a domain-specific AI processor along with its optimized software. This approach, together with our unique decentralized programming model, will enable customers to arm with the most powerful yet energy-efficient AI hardware as well as seamless integration. | $10M / Seed / Jul 21, 2021 | ||
| Standigm is a workflow AI-driven drug discovery company. Standigm has proprietary AI platforms encompassing novel target identification to lead generation to generate commercially valuable drug pipelines. Founded in 2015, the company has established an early-stage drug discovery workflow AI to generate First-in-Class lead compounds within sevenmonths. Pursuing full-stack, AI-driven industrializing drug discovery, Standigm has achieved the automation of molecular design workflow through DarkMolFactory™, and the automation effort has been expanding to the whole drug discovery process on the basis of Standigm AI platforms, including Standigm ASK™ for target discovery, Standigm BEST™ for lead design, and Standigm Insight™ for drug repurposing. | $36.4M / Series C / Mar 05, 2021 | ||
| See all 29 investments → | |||
Co-Investors
About Kakao Ventures
Kakao Ventures Corp. is an independent subsidiary of Kakao Corp. & the most active, startup-friendly early stage VC of Korea.
Kakao Ventures Contacts
| Name | Phone | Socials | |
|---|---|---|---|
Anne Ahn
Senior Associate
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Brian Jang
Director
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Jacob Kim
Director
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Jade Chung
Senior Associate
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Jun Kim
CEO
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FAQ
What does Jun Kim invest in?
Jun Kim invests primarily in Software, Machine Learning and Artificial Intelligence startups, most often at Series A and Seed stage. Most of the 29 investments tracked by Shizune back companies in South Korea. The Investment Focus section breaks down every industry, stage and country in the portfolio.
How do I contact Jun Kim?
Public channels for Jun Kim include LinkedIn. For a verified work email and phone number, create a free Shizune account — contact data is searched across 20+ sources and validated before delivery, so your outreach lands in the right inbox.
When did Jun Kim last invest?
The most recent investment recorded for Jun Kim closed in Oct 2026. Shizune tracks 29 investments in total for this profile, including round sizes, stages and portfolio companies, all sourced from confirmed public funding rounds and refreshed monthly.
Is Shizune's investor data accurate?
Yes — every stat on this profile is calculated from confirmed public funding rounds monitored across hundreds of sources since 2020. This profile was last refreshed in Oct 2026 and is updated monthly. Spotted something off? Use the “Suggest an edit” link near the top of the page.
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