AI Model Optimization Architect

Stack

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 closely 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 KV cache management, decoding behavior, and long-context performance.
• ▹Enable and optimize continuous batching (dynamic/iteration-level scheduling).
• ▹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.

Requirements

• ▹Expert-level expertise in PyTorch and inference-focused model optimization; strong Python engineering skills.
• ▹Hands-on experience with torch.compile/TorchDynamo or related graph capture and compilation workflows.
• ▹Deep understanding of transformer architectures, attention mechanisms, MoEs, and performance trade-offs.
• ▹Practical experience with KV cache 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.
• ▹Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field plus 4+ years of hardware, software, or systems engineering experience, OR a Master's degree plus 3+ years, OR a PhD plus 2+ years in a related field.

Nice to have

• ▹Experience developing fusion kernels using Triton or similar DSLs, 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.

Soft skills

What we offer

• ▹Annual base pay range of $158,400–$237,600.
• ▹Annual discretionary bonus program.
• ▹Opportunity for annual RSU grants.
• ▹Competitive benefits package.

About the company

Qualcomm's Cloud AI team develops hardware and software platforms that enable efficient inference of large-scale foundation models.