Infrastructure Engineer (ML Systems) - Paris
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
