Availability awaiting confirmation
We are waiting for a fresh update from the source. This page preserves the last received job details; current availability is not confirmed.
Job description
This physics AI startup is combining high-fidelity simulations and machine learning to compress engineering cycles from days to milliseconds at a fraction of the cost.
Here you would join a lean team of 10-15 engineers tasked with scaling Kubernetes-based infrastructure, improving reliability/performance for GPU workloads, and continuing to evolve their platform architecture as customer demand and compute requirements continue to grow.
Because the team is small, you will have greater ownership, scope, and impact on the platform's growth from day one.
Day-to-day responsibilities of the role vary, but can range from scaling cloud infrastructure (AWS/GCP), GPU orchestration (batch scheduling & queueing, multi-tenant resource allocation & autoscaling, fault-tolerant job execution), backend engineering (Python/Golang) or optimizing CI/CD and data pipelines.
This is a paint-your-own-path environment, suitable if you are looking for variety, autonomy, and rapid growth potential.
You'll need to be an expert in Kubernetes and have experience managing clusters. You are comfortable operating in cloud environments, have shipped backend systems, and have managed GPU workloads at scale.
Due to the nature of the client, candidates must be US citizens or Green Card holders.
#J-18808-Ljbffr
Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.