Wafer AI: maximize intelligence per watt; autonomous AI performance engineers optimizing inference on heterogeneous hardware. 7 people, $4M raised, SF.

Comp: $200K-$300K + 0.5%-1% equity. Location: on-site 5 days FiDi SF. Visa: Visa transfers (H-1B transfer, OPT); must already be in US; no new H1B.

Responsibilities

Ship day-zero support for new open-source models tuned for latency/throughput

Optimize serving stack: batching, KV cache, speculative decoding, quantization

Write/tune kernels in CUDA, HIP, Triton for NVIDIA, AMD, TPU, Trainium, D-Matrix

Design, deploy, operate heterogeneous multi-vendor clusters

Run production inference: reliability, observability, cost per token

Tech stack: CUDA, HIP, Triton, Python, TPU, AWS Trainium

Source: https://www.paraform.com/share/wafer-ai/cmrtyva4m000j0cl7i7j9wbue

Requirements

1-6 yrs SWE focused on systems close to hardware

GPU kernels (CUDA/HIP/Triton), inference serving optimization

First-principles, resourceful, high EQ; customer-facing comfort

On-site 5 days SF; must be in US