We are seeking a Staff Engineer – AI Model Optimization Architect to lead end-to-end model transformation and optimization for LLMs, VLMs, diffusion, and multimodal models on Qualcomm inference accelerators. This role works closely with compiler, performance, and accuracy teams to translate models into accelerator efficient execution while balancing throughput, latency, memory, and quality. The scope spans Day0 enablement through production deployment, with a strong emphasis on scaling optimizations to future architectures.

Architect and deliver model optimization strategies that transform PyTorch models for efficient inference on Qualcomm accelerators.

Drive graph capture and deployment using PyTorch, ONNX, and torch.compile, including model rewrites and graph-level transformations.

Design and implement fusion kernels using DSL based approaches (e.g., Triton), enabling fused operations and performance critical algorithmic rewrites.

Partner deeply with compiler, performance, and accuracy teams to co-design lowering strategies, kernel fusion, layout decisions, and runtime integration.

Profile and optimize LLM/VLM/diffusion inference for throughput and latency across batch sizes, sequence lengths, and serving modes.

Own transformer specific optimizations including KVcache management, decoding behavior, and long context performance.

Enable and optimize continuous batching (dynamic/iteration-level scheduling), understanding its impact on memory, scheduling, and tail latency.

Architect and scale distributed inference strategies (e.g., sharding and parallelism) across multi-core and multi-device systems.

Establish reusable approaches to scale model optimizations to new hardware architectures, creating robust patterns and tooling.

Debug complex performance or stability issues to root cause and drive production ready solutions.