Senior HPC Platform Engineer - Kubernetes & GPUs (Hybrid)

Arlequin AI; Paris; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Flexible remote policy: hybrid / full-

Equity stake

Training budget

Job summary

Arlequin AI is seeking an HPC Engineer to design and operate a cloud-native, scalable compute platform for its research and product workloads in Paris, ensuring low latency and high availability. You will manage GPU/CPU clusters on Kubernetes, implement end-to-end optimizations, and build self-service tools for engineers.

The role includes on-call duties during business hours. The team aims to grow from 3 to 8 by December, and the stack covers Scaleway, NVIDIA GPUs, K8s, and modern IaC tooling.

Qualifications

• 5+ years of experience in infrastructure / HPC / compute platforms, including production experience.
• Advanced proficiency with Kubernetes in a production environment.
• Hands-on experience with GPU workloads and batch schedulers: scheduling, resource management, and sharing.
• Experience operating a production service under latency and availability constraints: autoscaling, load management, SLOs, incident management.
• Strong IaC experience (Terraform / OpenTofu, ideally Terragrunt).
• Good understanding of distributed computing (multi-node / multi-GPU) and large-scale batch processing.

Responsibilities

• Infrastructure: design, deploy, and maintain infrastructure on Scaleway; industrialize IaC with OpenTofu and Terragrunt
• Data storage: design and operate high-performance storage (parallel/distributed file systems, data caches, object storage) and optimize end-to-end I/O for training and inference
• Compute platform: design, deploy, and operate a Kubernetes compute cluster sized for heterogeneous workloads; set up scheduling (queues, priorities, gang scheduling, fair-sharing); optimize GPU sharing and utilization; deploy and maintain device plugins
• Workloads: identify and eliminate bottlenecks across compute, network, and I/O; design and operate model serving (real-time, streaming, batch, progressive rollouts); operate large-scale batch pipelines and training pipelines with the research and MLOps teams
• Instrumentation & optimization: monitor compute and GPUs; instrument inference services (latency, throughput, error rate, SLOs); optimize compute costs with unit metrics; configure alerting; improve utilization and right-sizing
• Platform engineering: collaborate with research/product on the platform roadmap; support capacity planning; build self-service abstractions; document the platform and runbooks
• On-call: no rotation today, incidents are handled during business hours. When a rotation becomes necessary, it will never exceed one week on-call out of five, and will be compensated

Skills

Kubernetes in prod

GPU workloads

IaC (OpenTofu Terragrunt)

Distributed compute (MPI/NCCL)

Tools

Kubernetes tooling

OpenTofu

Terragrunt

ArgoCD

NCCL

MPI

RDMA

Job description

Arlequin AI is seeking an HPC Engineer to design and operate a cloud-native, scalable compute platform for its research and product workloads in Paris, ensuring low latency and high availability. You will manage GPU/CPU clusters on Kubernetes, implement end-to-end optimizations, and build self-service tools for engineers.

The role includes on-call duties during business hours. The team aims to grow from 3 to 8 by December, and the stack covers Scaleway, NVIDIA GPUs, K8s, and modern IaC tooling.