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Lunit Cancer Screening Tech Stack

AI-powered cancer screening software for radiology

Software Development Seattle 201–500 employees Public Company

Lunit builds FDA-cleared AI diagnostic tools for chest X-ray and mammography screening, deployed across 3,500+ clinical sites globally. The tech stack spans Python, Go, TypeScript, and Kubernetes with distributed GPU training infrastructure—a signal of heavy model-development and inference workload. Hiring velocity is accelerating, but the department mix reveals organizational tension: research (3), support (3), ops (2), and product (2) outpace engineering (1), suggesting product-market fit in clinical adoption has outrun internal platform capacity, and oncology biomarker development is pulling engineering resources away from core platform work.

Tech Stack 36 technologies

What Lunit Cancer Screening Is Building

Challenges

  • Integration challenges across diverse environments
  • Optimizing order-to-cash workflows
  • Inventory reconciliation accuracy
  • Developing ai-powered oncology biomarkers
  • Increasing demand in korean market
  • Large-scale medical data infrastructure
  • Gpu cluster training stability
  • Regulatory compliance for ai models
  • Clinical validation of ai models
  • Translating research to product

Active Projects

  • Process improvement projects across commercial operations
  • Integrated breast cancer screening ecosystem
  • Commercial development of ai-powered oncology biomarkers
  • Global business projects related to ai-powered oncology biomarkers
  • Clinical research of ai-powered oncology biomarkers
  • Bid and pre/post sales project support
  • Global ai platform integration for hospitals
  • Enhancing crm systems such as salesforce
  • Gpu cluster-based distributed training environment
  • Object detection

Hiring Activity

Accelerating15 roles · 10 in 30d

Department

Research
3
Support
3
Ops
2
Product
2
Data
1
Engineering
1
Manufacturing
1
Other
1

Seniority

Mid
5
Senior
4
Manager
3
Director
1
Intern
1
Junior
1

Notable leadership hires: Medical Director

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About Lunit Cancer Screening

Lunit is a Seoul-based public company developing medical AI software for cancer screening and detection. The product suite includes INSIGHT CXR (chest X-ray), INSIGHT MMG (2D mammography), INSIGHT DBT (digital breast tomosynthesis), and CXR Triage (emergency department triage in the US). The company's AI models have been validated across 100+ peer-reviewed publications and are integrated into radiology workflows at 3,500+ sites across the US, South Korea, Japan, and other markets. Commercial operations span bid support, sales engineering, and hospital platform integration, while research and clinical teams are actively developing AI-powered oncology biomarkers for expansion beyond screening into treatment.

HeadquartersSeattle
Company Size201–500 employees
Hiring MarketsSouth Korea, Japan, United States

Frequently Asked Questions

What is Lunit's tech stack?

Python, Go, TypeScript, React, PostgreSQL, Kafka, Kubernetes, Docker, AWS, GCP, Azure. Salesforce for CRM, PACS and HL7 for medical imaging/interoperability, Argo CD for deployment, Prometheus/Grafana/Loki for observability, Apache Airflow and Prefect for data pipelines.

How many sites use Lunit's AI for cancer screening?

3,500+ clinical sites globally deploy Lunit's AI products (INSIGHT CXR, MMG, DBT). The software is validated through 100+ peer-reviewed publications in journals including JAMA Oncology, Lancet Digital Health, and Radiology.

Is Lunit hiring engineers?

Lunit has 16 active roles with accelerating hiring velocity. Engineering openings are limited (1 posted role), but research (3), support (3), ops (2), and product (2) are actively recruiting across the US, South Korea, and Japan.

How this profile is built

Lunit Cancer Screening's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.