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# Software Engineer in Test (SDET)

- **Company:** Trust In Soda
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Continuous Delivery, Distributed Systems, Machine Learning, Software Reliability Testing, Graphics Processing Unit (GPU), Large Language Models, Kubernetes Helm Charts, Backend, AI Platforms, Kubernetes, Front End Software Development, SDET, Golang, Microservices
- **Published:** July 16, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p51oqbcd7i

## About the Role

Looking for a Senior SDET who actually enjoys breaking complex distributed systems and wants to scale test frameworks for platforms running AI, LLM, and GPU workloads, * ing:Strong, production-level experience building backend test frameworks in Gol

 * ang.Deep knowledge of Kubernetes, containers, and microservices testing strateg
 * ies.Solid experience testing secure APIs (gRPC and RE
 * ST).An autonomous, solution-oriented mindset-someone who uses metrics and observability to drive continuous delivery confide
 * _nce.Nice to h_ave: Exposure to modern DevOps workflows, or automated testing for AI platforms/GPU workloads (vLLM, KServe, et

## Description

I'm working with a really impressive, fast-growing tech business that completely throws corporate ego out the window in favor of actual engineering quality and creativity. They need a heavy-hitting Senior SDET with strong Golang and Kubernetes experience to join their core product tea

m.There's no rigid blueprint here. If you're the type of engineer who spots a bottleneck and constructively shakes things up, you'll fit right i

n.
What you'll actually be doi

 * ng:Building from scratch: Designing and maintaining robust automated test frameworks and quality engineering solutions using
 * Go.AI Infrastructure: Laying down automated testing strategies across API (gRPC/REST), integration, performance, and reliability testing for systems powering massive model training and inferen
 * ce.Cloud-Native stuff: Testing and deploying solutions on Kubernetes, spinning up Helm charts, and working directly with the Kubernetes A
 * PI.Shaping the design: Sitting in on architectural discussions with backend, ML, and frontend teams to make sure scalability and testability are baked in before a single line of feature code gets writt


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