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 likeSuccess 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 doingRunning Kubernetes clusters for GPU training and inferenceImproving how GPU work is scheduled and placedDetecting, draining and repairing unhealthy GPU nodesManaging capacity with our cloud providersDebugging failures in GPUs, Linux, storage and networkingThe first yearBy 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 withYour 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 takesYou’re likely a good fit if you:Have built and run GPU workloads on KubernetesCould design a GPU cluster for training and online RL from scratchDebug confidently at the OS layer and below (Linux, GPU drivers, networking and storage)Write strong PythonIt’s a plus if you’ve worked as an SRE or infrastructure engineer, or have used:SlurmKubernetes scheduling and autoscaling tools such as Kueue or KarpenterMulti-cloud orchestration to.