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AI Inference Infrastructure Software Engineer (Kubernetes / Cloud)
ElastixAI
Seattle, USA
Full-time
Mid · 3+ years experience
Hybrid
Discovered 1 weeks ago
KubernetesAWSGCPTerraformPulumiAnsible
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Role Overview
We're looking for an Inference Infrastructure Software Engineer to own and evolve the cloud and Kubernetes backbone behind our Token-as-a-Service platform. You'll be the connective tissue between our inference engine and the production environments where customers actually consume tokens — making sure our accelerated workloads run reliably, scale predictably, and deploy seamlessly across managed and self-hosted clusters.
This is a hands-on role with broad surface area. You'll touch everything from cluster bring-up, automating the software releases, and AI Accelerator scheduling to service reliability and cost optimization, working closely with our ML, runtime, and hardware teams to expose the full performance of our co-designed stack to end users.
Key Skills for This Role
KubernetesAWSGCPTerraformPulumiAnsible
Full Job Posting
Location
- Seattle, WA (Hybrid - 3 days/week in office)
About ElastixAI
ElastixAI is an early-stage Software startup on a mission to reinvent AI inference infrastructure from the ground up. We're building a next-generation inference platform that delivers unprecedented efficiency by tightly integrating machine learning, software stack, and custom hardware. Our philosophy is simple: the best performance comes from holistic co-design, where every layer, from model architecture to kernels to silicon, works in harmony.
If you're excited about pushing AI performance to physical limits and shaping the future of large-scale inference, we'd love to meet you.
Role Summary
We're looking for an Inference Infrastructure Software Engineer to own and evolve the cloud and Kubernetes backbone behind our Token-as-a-Service platform. You'll be the connective tissue between our inference engine and the production environments where customers actually consume tokens — making sure our accelerated workloads run reliably, scale predictably, and deploy seamlessly across managed and self-hosted clusters.
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