Frontier Tech Caching Gpu Jobs

Own GPU operational readiness.

, KV-cache efficiency, and tensor/expert parallelism.

flow for GPUs, detects bugs early, and generates automated alerts to maintain the GPU stack's functionality and performance Create profiler and debugger

, and distributed GPU workloads.

Own GPU operational readiness.

for the physical and server infrastructure that powers neural recording training, data processing, and engineering workloads: the data center floor itself, the GPU

This role will require a strong foundation in computational science, parallel algorithms, and the specific intricacies of GPU and CPU optimization.

meet strict performance requirements such as producing necessary outputs within strict latency targets and using the allocated amount of compute resources (CPU/GPU

Experience with writing GPU kernels and evaluation/debugging of GPU workloads.

Science or Computer Engineering or equivalent practical experience 10+ years on designing/architecting complex, high performance architectures - CPUs, GPUs

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs.

the Role Lumbra is building Nebula, an agentic harness running as a set of microservices on managed Kubernetes, backed by managed databases, caching

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs.

Demonstrated experience managing GPU clusters or large-scale distributed compute infrastructure.