AI Model Optimization Architect

Staff Engineer – Ai Model Optimization Architect

Qualcomm is leveraging its strengths in compute, connectivity, and AI acceleration to play a central role in the evolution of Cloud AI. The Qualcomm Cloud AI team develops hardware and software platforms enabling efficient inference of large-scale foundation models.

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.

Key Responsibilities

• 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.

Required Qualifications

• Expert level expertise in PyTorch and inference focused model optimization; strong Python engineering skills.
• Hands on experience with torch.compile / Torch Dynamo or related graph capture and compilation workflows.
• Deep understanding of transformer architectures, attention mechanisms, MoEs, and performance trade-offs.
• Practical experience with KVcache behavior, serving time optimizations, and memory/performance tradeoffs.
• Strong foundation in computer architecture, ML accelerators, and distributed systems.
• Proven ability to lead cross-functional technical efforts and influence design decisions.
• MS in Computer Science, Machine Learning, Computer Engineering, or Electrical Engineering, or equivalent experience.

Preferred / Bonus Qualifications

• Experience developing fusion kernels using Triton or similar DSLs, and collaborating with ML compiler teams.
• Familiarity with LLM serving stacks and continuous batching systems.
• Background in numerical methods, performance/accuracy trade-off analysis, or evaluation frameworks.
• PhD in a relevant field.

Minimum Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR
• Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Pay range and Other Compensation & Benefits :

$ - $

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation  also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus).

In addition, our highly competitive benefits package is designed to support your success at work, at home, and r recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.