Senior MLOps Engineer
Software Development AI/Artificial Intelligence AI/ML Hardware Machine Learning
年収
Location
Benefits
13ヶ月目の給与
フル社会保険
その他の福利厚生
年間給与の見直し
旅行/会社の旅行
仕事用のノートパソコン/デスクトップ
業績ボーナス
追加の健康保険
Job Overview And Responsibility
- Own the success-run ratio of GPU workloads as a measurable SLO; drive it up and keep it there. - Build and operate the GPU job scheduling and queueing layer — fair-share allocation, prioritization, backpressure, and recovery across a heterogeneous fleet. - Implement GPU partitioning and sharing (MIG, MPS, time-slicing) to raise utilization without destabilizing runs. - Profile and right-size workloads: per-model GPU memory, runtime, and failure characteristics; eliminate OOMs and silent failures. - Define a standard packaging/deployment contract for new models so onboarding is repeatable, not bespoke. - Build observability for the run lifecycle — metrics, logs, traces, alerting — so failures are caught and diagnosed fast. - Harden the orchestration stack (workflow engine, durable execution, retries/failover) against real failure modes. - Partner with the DevOps engineer on cluster/networking and with AI engineers to make their models production-ready.
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.
Preferred skills and experiences
- Exposure to scientific or research workflows, ideally drug discovery or other iterative experimental loops. - Experience mentoring or setting technical direction for a small team. - Background in RAG, memory systems, or multi-agent orchestration; bio/chem familiarity a plus.
Report to
CEO
Interview process
1-2 rounds of interview
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