Description review
Infrastructure Engineer (ML Compute) - Paris
H Company · Remote - Hybrid Paris, Hybrid London · back to the listing
HR standards
80/100
solid
Title ↔ description
73/100
solid
Reads as
Site Reliability Engineer
93% confident
What this role officially is
ICT system administrator — ESCO, the EU occupation classification
ICT system administrators are responsible for the upkeep, configuration, and reliable operation of computer and network systems, servers, workstations and peripheral devices. They may acquire, install, or upgrade computer components and software; automate routine tasks; write computer programs; troubleshoot; train and supervise staff; and provide technical support. They ensure optimum system integrity, security, backup and performance.
Also known as: enterprise administrator, IT system administrator, ICT systems administrator, ICT sysadmins, IT systems administrator, ICT sysadmin
How others title the same work
Large employers
- Senior Site Reliability / Gitops Engineer Canonical
- Senior Site Reliability / Gitops Engineer Canonical Ltd.
- Senior Site Reliability Engineer Canonical Ltd.
- Site Reliability / Gitops Engineer Canonical Ltd.
- Site Reliability Engineer Canonical Ltd.
Startups
- Senior Software Engineer, Product Infrastructure Atlas
- Site Reliability Engineer Beam
What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
You’ll own the GPU compute that powers our model training, evaluation and serving systems.
This role sits in our Infrastructure team, which works at the lowest layers of our stack, from Kubernetes down to the GPU hardware. You’ll run the clusters, GPUs and schedulers that our research, product and platform teams depend on, and manage the capacity we buy from our cloud providers.
What success looks like
Success is measured by how well we use our GPUs: keeping production reliable and improving the performance of our training and serving stack.
What you’ll be doing
• Running Kubernetes clusters for GPU training and inference
• Improving how GPU work is scheduled and placed
• Detecting, draining and repairing unhealthy GPU nodes
• Managing capacity with our cloud providers
• Debugging failures in GPUs, Linux, storage and networking
The first year
By month 3, you’ll be handling a share of cluster incidents on your own and will have shipped one change to scheduling or node health, such as automatically detecting and draining unhealthy GPU nodes. By month 6, you’ll own one part of the compute layer outright: scheduling and placement, node health and repair, or capacity and new regions. By month 12, you’ll be responsible for a globally distributed GPU system that shifts capacity between training and inference as needs change.
Who you’ll work with
Your hiring manager is our VP of Engineering. Day to day, you’ll work with the Platform Infrastructure team, the research and product teams who use our compute, and our cloud providers and other vendors.
What we think it takes
You’re likely a good fit if you:
• Have built and run GPU workloads on Kubernetes
• Could design a GPU cluster for training and online RL from scratch
• Debug confidently at the OS layer and below (Linux, GPU drivers, networking and storage)
• Write strong Python
It’s a plus if you’ve worked as an SRE or infrastructure engineer, or have used:
• Slurm
• Kubernetes scheduling and autoscaling tools such as Kueue or Karpenter
• Multi-cloud orchestration tools such as SkyPilot
• AWS services such as EKS or SageMaker HyperPod
• High-performance interconnects and collective communication libraries such as EFA, InfiniBand, NCCL or Open MPI
• Terraform
You don’t need ML research experience. If you match most of this but not every point, please apply.
How we hire
Four stages, about three hours in total: a 30-minute call with our Talent team, a 60-minute coding interview, a 60-minute system design interview and a closing 30-minute conversation with our VP of Engineering. The coding and system design interviews are modelled on real-world problems you’d face in this role.
Practicalities
The role is hybrid, with three days a week in our Paris or London office and occasional travel between the two. We offer a competitive package.
This role sits in our Infrastructure team, which works at the lowest layers of our stack, from Kubernetes down to the GPU hardware. You’ll run the clusters, GPUs and schedulers that our research, product and platform teams depend on, and manage the capacity we buy from our cloud providers.
What success looks like
Success is measured by how well we use our GPUs: keeping production reliable and improving the performance of our training and serving stack.
What you’ll be doing
• Running Kubernetes clusters for GPU training and inference
• Improving how GPU work is scheduled and placed
• Detecting, draining and repairing unhealthy GPU nodes
• Managing capacity with our cloud providers
• Debugging failures in GPUs, Linux, storage and networking
The first year
By month 3, you’ll be handling a share of cluster incidents on your own and will have shipped one change to scheduling or node health, such as automatically detecting and draining unhealthy GPU nodes. By month 6, you’ll own one part of the compute layer outright: scheduling and placement, node health and repair, or capacity and new regions. By month 12, you’ll be responsible for a globally distributed GPU system that shifts capacity between training and inference as needs change.
Who you’ll work with
Your hiring manager is our VP of Engineering. Day to day, you’ll work with the Platform Infrastructure team, the research and product teams who use our compute, and our cloud providers and other vendors.
What we think it takes
You’re likely a good fit if you:
• Have built and run GPU workloads on Kubernetes
• Could design a GPU cluster for training and online RL from scratch
• Debug confidently at the OS layer and below (Linux, GPU drivers, networking and storage)
• Write strong Python
It’s a plus if you’ve worked as an SRE or infrastructure engineer, or have used:
• Slurm
• Kubernetes scheduling and autoscaling tools such as Kueue or Karpenter
• Multi-cloud orchestration tools such as SkyPilot
• AWS services such as EKS or SageMaker HyperPod
• High-performance interconnects and collective communication libraries such as EFA, InfiniBand, NCCL or Open MPI
• Terraform
You don’t need ML research experience. If you match most of this but not every point, please apply.
How we hire
Four stages, about three hours in total: a 30-minute call with our Talent team, a 60-minute coding interview, a 60-minute system design interview and a closing 30-minute conversation with our VP of Engineering. The coding and system design interviews are modelled on real-world problems you’d face in this role.
Practicalities
The role is hybrid, with three days a week in our Paris or London office and occasional travel between the two. We offer a competitive package.