MLOps Engineer (3–7 yrs), Seoul at a Medical AI Startup Backed by KAIST Ventures & SNU Holdings

About The Job The client is hiring for this role, and Harper, an AI career agent, is on your side of the table. Skip the application form: just talk to Harper, and in a few minutes it learns your background, then puts the strongest profiles directly in front of the client's decision-makers. 👉 Start with Harper Job Title MLOps Engineer (GPU Training & Inference Infrastructure), Seoul Level 3–7 years of hands-on MLOps, ML platform, or backend/infrastructure engineering. Individual contributor who owns the GPU training and inference stack end to end. Salary Competitive salary plus stock options. Meals, in-office amenities, and a self-development allowance. Location Seoul, South Korea (On-site) Company Description The client is a Seoul-based medical AI startup founded in 2023 by a team from Seoul National University College of Medicine. It builds AI that reads EEG (brain-wave) data to quantify mental health and predict how patients respond to psychiatric and neurological drugs, so that prescribing rests on measured signals rather than subjective judgment alone. The company has built its own EEG measurement device and is now developing world models, biomedical AI, and LLM/agent systems on top of its clinical data. Its seed round was led by KAIST Ventures, joined by MYSC, GD Ventures, the Impact Foundation, and the former CEO of a listed Korean medical AI company. In March 2026 it added a strategic investment from Seoul National University Technology Holdings. Next on the roadmap: supporting pharma clinical R&D and clinician decision-making, and AI agents for psychiatry and neurology clinics in the US. What You'll Do Build and run GPU training and inference infrastructure for world models, biomedical AI, and LLM/agent systems Build the ML pipeline and MLOps environment that connects training, evaluation, model registry, and deployment Build experiment management: tracking and reproducing configs, datasets, checkpoints, metrics, and artifacts Schedule, monitor, and optimize GPU resources across on-premise and cloud Build and harden API serving, batch inference, and online inference for LLM/agent and AI models Build post-training pipelines (SFT, preference learning, RL) and agent evaluation environments Work with AI scientists, data engineers, and software engineers to turn research code into reproducible, scalable systems Who You Are Hands-on experience in MLOps, ML platform, or backend/infrastructure engineering Python-based ML or backend system development Docker and Kubernetes: you have built and operated them, not just used them You understand training and inference workflows in PyTorch or a similar framework You run Linux and GPU-based ML infrastructure and can debug it when it breaks You can take a researcher's requirement and turn it into a working system Stack: PyTorch, Python, AWS, Docker, CUDA, Kubernetes, Kubeflow, Grafana, Prometheus, Terraform, MLflow, GitHub Actions Nice to have: MLflow / Weights & Biases / Kubeflow; multi-GPU or distributed training (DDP, DeepSpeed, FSDP, Ray); vLLM or Triton serving; Airflow / Argo; hybrid cloud + on-premise Why Join You own the ML infrastructure of a company whose models answer real clinical questions, from the first GPU to production serving A medical team from Seoul National University and investors from KAIST and SNU behind the science Small team, early stage: the systems you build become the company's standard How This Works Harper is an AI career agent on your side, free for candidates and paid by companies only when someone gets hired. Talk to Harper for a few minutes. It learns your background, no resume form required Harper puts the strongest profiles directly in front of the client's decision-makers If the client wants to meet, you decide whether to take the conversation Why the company is unnamed: our clients ask us not to name them publicly. You'll get the name as soon as you connect. Show more Show less