Senior MLOps Engineer

Required Skills and Experience

- 5+ years in MLOps / ML platform / GPU systems engineering, with direct ownership of production reliability. - Deep experience operating GPU workloads at scale (NVIDIA stack: CUDA, drivers, GPU Operator, MIG/MPS). - Strong background in workload orchestration and scheduling — Kubernetes (Jobs/batch), Ray, Slurm, or equivalent. - Hands-on managed-ML platform experience on at least one major cloud (GCP: Cloud Run, Vertex AI; AWS: SageMaker), with working familiarity of the other. - Solid understanding of cloud architecture (compute, networking, storage, IAM) across hybrid cloud + on-prem. - Proven track record raising reliability/utilization of a heterogeneous GPU fleet. - Solid software engineering (Python and one systems language) — you build platform tooling, not just configure it. - Observability and SRE fundamentals: SLOs, metrics, tracing, incident response.

Why Candidate should apply this position

- Competitive salary (negotiable based on experience) - Build a professional network through collaborations with pharmaceutical companies, industry leaders, and academic experts. - Work on impactful projects addressing critical challenges in drug discovery and healthcare. - 2 work-from-home days per month, plus daily lunch provided by the company. - Holiday & Tet bonuses; performance-based bonus. - Social insurance contribution on full salary.