"Conquering cancer through AI" Lunit, a portmanteau of ‘Learning unit,’ is a medical AI software company devoted to providing AI-powered total cancer care.Our AI solutions help discover cancer and predict cancer treatment outcomes, achieving timely and individually-tailored cancer treatment.🗨️ About the TeamWho will I spend 8+ hours/day with?You will join the AI Innovation team behind the Chain-of-Evidence (CoE) project, also known as 특화 파운데이션 모델 project. The team brings together AI research scientists and engineers across RAG & Product, Model & Evaluation, and Data & Knowledge, building real products while tackling meaningful applied research along the way.Members have diverse backgrounds and interests — some are passionate about LLM training and evaluation, others about retrieval and knowledge systems, others about productization and clinical deployment — united by a shared commitment to improving patient care.This team is a fast paced “full stack” team, where ideation to deployable PoC happens in rapid iteration cycles. The team gets to work on strategically important initiatives aligned to enabling new business opportunities for Lunit.🗨️ About the PositionWhat will make me proud to work here?You will have a direct impact on bringing trustworthy, evidence-grounded AI into real clinical workflows — clinical intelligence and agentic applications — deployed and evaluated together with partner hospitals.Your work will push forward the performance of Lunit's medical foundation models, RAG systems, and agentic pipelines that power Lunit’s next generation clinical intelligence.We work with large-scale clinical knowledge sources (medical literature, clinical guidelines, insurance and EMR data) and the cloud/GPU compute to leverage them.Current work spans LLM post-training (SFT/SDFT), retrieval-augmented generation, agentic reasoning, faithfulness and citation evaluation, and live clinical deployment — among others.As an early-career scientist, you will grow fast — owning real features end-to-end, learning from a strong multi-disciplinary team, and meeting the actual medical professionals who will be using our systems.🚩 Roles & ResponsibilitiesBuild, implement, and help design components of ‘Clinical Intelligence’: medical foundation models, LLM- and retrieval-based AI systems that address real-world clinical problems.Rapidly turn ideas into working prototypes and deployable PoCs, iterating quickly with the team.Contribute to the core components of ‘Clinical Intelligence’ — medical RAG, agentic reasoning pipelines, context management, LLM post-training (SFT/SDFT/RL), and evaluation of faithfulness, citation quality, and hallucination.Write high-quality, well-tested code in our internal research codebases, following solid engineering practices.Collaborate with clinical partners (hospitals, medical doctors) and product teams to ship systems clinicians actually use.Requirements🎯 Qualifications2+ years of industry/research experience in machine learning or deep learning, or a strong portfolio of built-and-shipped projects (new graduates with demonstrated building experience are welcome), with a preference for natural language processing, large language models, information retrieval, or medical AI.Hands-on proficiency in Python and modern ML frameworks (e.g., PyTorch, Hugging Face Transformers), with working knowledge of LLM, NLP, and retrieval techniques and the ability to build end-to-end (e.g., vLLM, RAG frameworks such as LlamaIndex or LangChain).Demonstrated track record of building and shipping working systems — side projects, open-source, internships, hackathons, or competitions.Taste for automation and engineering quality.‘Bias towards action’ — a strong builder instinct; you ship.Fast learner, comfortable with ambiguity and rapid iteration.Collaborative team player.Capable of handling day-to-day business communication in English (written or verbal).Highly responsible, with an eye for detail and motivated to build high-quality, reliable solutions following current best practices.Strong motivation to work on medically impactful problems and contribute to advancing the standard of care through AI.🏅 Preferred ExperiencesExperience working with clinical, biomedical, or otherwise high-stakes text data (ie. EMR).Hands-on experience with one or more of: LLM fine-tuning / post-training (SFT, DPO/RLHF), retrieval-augmented generation systems, agentic / tool-using LLM pipelines, or LLM evaluation frameworks.Experience deploying a system into real-world production (cloud or on-premise).Experience with context engineering / context management for large-context LLM systems — structuring and orchestrating knowledge, tools, and state across an agent's context window.Up-to-date awareness of emerging trends and recent advances in LLM, agent, and retrieval research.Contributions to open-source repositories in NLP, LLMs, RAG, or related areas.Able to engage with customers (clinicians, hospital staff) to define problems and design systems.📝How to ApplyCV (English or Korean, free format)Any other relevant material (English or Korean) *optionalLinks to a portfolio, GitHub, or shipped projects are strongly encouraged.🏃♀️ Hiring ProcessDocument Screening → Introductory Interview → Technical Interview → Culture-fit Interview → OnboardingAfter the final interview, we may proceed with reference checks if needed🤝 Work Conditions and EnvironmentWork Type: Full-Time(3-month probation)Work Location : Lunit HQ (5F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea)Salary: After Negotiation🎸 ETCIf you misrepresent your experience or education or provide false or fraudulent information in or with your application, it may be grounds for cancellation of the employment.Lunit is committed in providing the preferential processing to those eligible for employment protection (national merits and people with disabilities) relevant to related laws and regulations.Benefits🌻Benefits & PerksThe office is at a very convenient location, just a minute away from Gangnam Station Exit 3.Meal Allowance is provided (up to 12,000 KRW per meal) when working at the office.Latest computer models, such as Macs and 4K monitors are provided and can be renewed every three years.Seminar registration fees and book purchases are covered.Regular in-house AI and medical seminars are held.In-house English lessons (aka Luniversal) is provided for English development.Access to high-quality AI learning resources & deep learning DevOps system.Up to 1.2 million KRW worth of benefits points can be claimed annually.Holiday Allowances are provided in the form of gifts or vouchers for Korean National holidays, Seollal and Chuseok.Congratulatory and Condolence allowances, along with paid time off are provided.Annual medical checkups and employee accident insurance are provided.
"Conquering cancer through AI"
Lunit, a portmanteau of ‘Learning unit,’ is a medical AI software company devoted to providing AI-powered total cancer care.
Our AI solutions help discover cancer and predict cancer treatment outcomes, achieving timely and individually-tailored cancer treatment.
🗨️ About the Team
Who will I spend 8+ hours/day with?
- You will join the AI Innovation team behind the Chain-of-Evidence (CoE) project, also known as 특화 파운데이션 모델 project. The team brings together AI research scientists and engineers across RAG & Product, Model & Evaluation, and Data & Knowledge, building real products while tackling meaningful applied research along the way.
- Members have diverse backgrounds and interests — some are passionate about LLM training and evaluation, others about retrieval and knowledge systems, others about productization and clinical deployment — united by a shared commitment to improving patient care.
- This team is a fast paced “full stack” team, where ideation to deployable PoC happens in rapid iteration cycles. The team gets to work on strategically important initiatives aligned to enabling new business opportunities for Lunit.
🗨️ About the Position
What will make me proud to work here?
- You will have a direct impact on bringing trustworthy, evidence-grounded AI into real clinical workflows — clinical intelligence and agentic applications — deployed and evaluated together with partner hospitals.
- Your work will push forward the performance of Lunit's medical foundation models, RAG systems, and agentic pipelines that power Lunit’s next generation clinical intelligence.
- We work with large-scale clinical knowledge sources (medical literature, clinical guidelines, insurance and EMR data) and the cloud/GPU compute to leverage them.
- Current work spans LLM post-training (SFT/SDFT), retrieval-augmented generation, agentic reasoning, faithfulness and citation evaluation, and live clinical deployment — among others.
- As an early-career scientist, you will grow fast — owning real features end-to-end, learning from a strong multi-disciplinary team, and meeting the actual medical professionals who will be using our systems.
🚩 Roles & Responsibilities
- Build, implement, and help design components of ‘Clinical Intelligence’: medical foundation models, LLM- and retrieval-based AI systems that address real-world clinical problems.
- Rapidly turn ideas into working prototypes and deployable PoCs, iterating quickly with the team.
- Contribute to the core components of ‘Clinical Intelligence’ — medical RAG, agentic reasoning pipelines, context management, LLM post-training (SFT/SDFT/RL), and evaluation of faithfulness, citation quality, and hallucination.
- Write high-quality, well-tested code in our internal research codebases, following solid engineering practices.
- Collaborate with clinical partners (hospitals, medical doctors) and product teams to ship systems clinicians actually use.
Requirements
🎯 Qualifications
- 2+ years of industry/research experience in machine learning or deep learning, or a strong portfolio of built-and-shipped projects (new graduates with demonstrated building experience are welcome), with a preference for natural language processing, large language models, information retrieval, or medical AI.
- Hands-on proficiency in Python and modern ML frameworks (e.g., PyTorch, Hugging Face Transformers), with working knowledge of LLM, NLP, and retrieval techniques and the ability to build end-to-end (e.g., vLLM, RAG frameworks such as LlamaIndex or LangChain).
- Demonstrated track record of building and shipping working systems — side projects, open-source, internships, hackathons, or competitions.
- Taste for automation and engineering quality.
- ‘Bias towards action’ — a strong builder instinct; you ship.
- Fast learner, comfortable with ambiguity and rapid iteration.
- Collaborative team player.
- Capable of handling day-to-day business communication in English (written or verbal).
- Highly responsible, with an eye for detail and motivated to build high-quality, reliable solutions following current best practices.
- Strong motivation to work on medically impactful problems and contribute to advancing the standard of care through AI.
🏅 Preferred Experiences
- Experience working with clinical, biomedical, or otherwise high-stakes text data (ie. EMR).
- Hands-on experience with one or more of: LLM fine-tuning / post-training (SFT, DPO/RLHF), retrieval-augmented generation systems, agentic / tool-using LLM pipelines, or LLM evaluation frameworks.
- Experience deploying a system into real-world production (cloud or on-premise).
- Experience with context engineering / context management for large-context LLM systems — structuring and orchestrating knowledge, tools, and state across an agent's context window.
- Up-to-date awareness of emerging trends and recent advances in LLM, agent, and retrieval research.
- Contributions to open-source repositories in NLP, LLMs, RAG, or related areas.
- Able to engage with customers (clinicians, hospital staff) to define problems and design systems.
📝How to Apply
- CV (English or Korean, free format)
- Any other relevant material (English or Korean) *optional
- Links to a portfolio, GitHub, or shipped projects are strongly encouraged.
🏃♀️ Hiring Process
- Document Screening → Introductory Interview → Technical Interview → Culture-fit Interview → Onboarding
- After the final interview, we may proceed with reference checks if needed
🤝 Work Conditions and Environment
- Work Type: Full-Time(3-month probation)
- Work Location : Lunit HQ (5F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea)
- Salary: After Negotiation
🎸 ETC
- If you misrepresent your experience or education or provide false or fraudulent information in or with your application, it may be grounds for cancellation of the employment.
- Lunit is committed in providing the preferential processing to those eligible for employment protection (national merits and people with disabilities) relevant to related laws and regulations.
Benefits
🌻Benefits & Perks
- The office is at a very convenient location, just a minute away from Gangnam Station Exit 3.
- Meal Allowance is provided (up to 12,000 KRW per meal) when working at the office.
- Latest computer models, such as Macs and 4K monitors are provided and can be renewed every three years.
- Seminar registration fees and book purchases are covered.
- Regular in-house AI and medical seminars are held.
- In-house English lessons (aka Luniversal) is provided for English development.
- Access to high-quality AI learning resources & deep learning DevOps system.
- Up to 1.2 million KRW worth of benefits points can be claimed annually.
- Holiday Allowances are provided in the form of gifts or vouchers for Korean National holidays, Seollal and Chuseok.
- Congratulatory and Condolence allowances, along with paid time off are provided.
- Annual medical checkups and employee accident insurance are provided.
"Conquering cancer through AI"Lunit, a portmanteau of ‘Learning unit,’ is a medical AI software company devoted to providing AI-powered total cancer care.Our AI solutions help discover cancer and predict cancer treatment outcomes, achieving timely and individually-tailored cancer treatment.
"Conquering cancer through AI"
Lunit, a portmanteau of ‘Learning unit,’ is a medical AI software company devoted to providing AI-powered total cancer care.
Our AI solutions help discover cancer and predict cancer treatment outcomes, achieving timely and individually-tailored cancer treatment.
