The work is heavy software engineering: you will design and ship control-plane services, scheduler integrations, autoscaling controllers, the inference-serving platform, RBAC and quota systems, observability and cost tooling, and the CLI and APIs ML engineers…
This is the surface area of the platform.
You won't own all of it at once - you'll take a capability and build it end to end - but you should be…
The serving platform. The control plane that turns a model artifact into a scalable, multi-tenant endpoint: intelligent routing and load balancing across replicas…
The scaling and elasticity layer. Autoscaling for both training (elastic and gang scaling, scale-to-fit) and serving (queue-depth and utilization-driven), capacity pooling across clusters…
Strong software engineering in Go or Python, with the judgment to build maintainable systems and the depth to debug them in production.
Kubernetes at the controller and internals level - you have written operators or controllers, understand the scheduler and API machinery…
Working literacy in the GPU-specific platform constraints: MIG and GPU sharing, gang scheduling, topology- and fabric-aware placement…
A product mindset toward internal users - you design APIs and abstractions people want to use…
Experience: 5+ years · Job type: Full-time
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