Senior Staff Software Engineer, AI Model Enablement & Software Tools

Company

Qualcomm Technologies, Inc.

Job Area

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary

As a leading technology innovator, Qualcomm pushes the boundaries of what is possible to enable next-generation experiences and drive digital transformation, creating a smarter, connected future for all.

As a Senior Staff Software Engineer in the Qualcomm AI Stack SDK Software team, you will provide technical leadership for bringing state-of-the-art AI models to Snapdragon platforms using Qualcomm’s AI software stack and model-enablement frameworks.

You will lead the analysis, adaptation, optimization, quantization, compilation, and execution of modern model architectures, including Large Language Models (LLMs), Large Vision Models (LVMs), Vision-Language Models (VLMs), and Large Multimodal Models (LMMs). You will work closely with AI Research teams to understand model internals, identify operator- and graph-level gaps against Qualcomm AI accelerator backends, and develop scalable solutions that can be reused across model families.

The role operates at the intersection of model architecture, machine learning algorithms, graph transformations, quantization, compiler and runtime technologies, and hardware-aware optimization. You will define technical direction and improve the Qualcomm AI software stack and model-enablement frameworks through reusable model adaptations, quantization recipes, graph optimizations, compilation flows, and execution interfaces.

You will collaborate with ML Research, AI accelerator hardware and software teams, compiler and runtime teams, Product Management, Program Management, QA, and customer engineering teams to take new model architectures from initial analysis through production-quality execution on Qualcomm devices.

This is a hands-on leadership role. You will lead and manage a small team of engineers while continuing to contribute as an individual contributor, drive architecture and implementation, establish engineering priorities, mentor team members, and own delivery across multiple model-enablement initiatives.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ 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 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR

PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Responsibilities

• Lead the bring-up and enablement of new LLM, LVM, VLM, and multimodal model architectures on Snapdragon platforms.
• Partner with AI Research teams to understand model architecture, tensor flows, attention and cache behavior, model inputs and outputs, and performance-sensitive computation patterns.
• Analyze model graphs and identify operator-level, graph-level, precision, memory, and runtime gaps against Qualcomm AI accelerator backends.
• Own the architecture and design of scalable model-enablement solutions, including component boundaries, APIs, stage contracts, configuration schemas, artifact interfaces, extensibility mechanisms, and integration across model, quantization, compiler, and runtime systems.
• Define scalable approaches for model re-authoring, graph adaptation, graph lowering, decomposition, fusion, layout transformation, and backend-specific optimization.
• Architect reusable model-enablement solutions instead of model-specific workarounds, and integrate them into Qualcomm’s AI software stack and model-enablement frameworks.
• Lead the design of declarative and extensible pipeline capabilities, including reusable stages, model mappings, quantization recipes, configuration interfaces, artifact handoffs, caching, validation, and execution workflows.
• Develop and optimize model preparation, quantization, calibration, export, compilation, and on-device execution flows.
• Apply quantization techniques such as post-training quantization, quantization-aware training, mixed precision, weight and activation quantization, blockwise or groupwise quantization, calibration, and accuracy-recovery optimization.
• Evaluate accuracy, latency, throughput, memory usage, model size, and power efficiency across floating-point, prepared, quantized, compiled, and on-device execution checkpoints.
• Guide enablement of advanced Generative AI features such as KV-cache optimization, prefill and decode graphs, LoRA, Mixture-of-Experts, speculative decoding, multimodal processing, and vision encoders.
• Debug complex issues across model architecture, PyTorch and ONNX graphs, quantization, compiler, runtime, operating system, and hardware layers.
• Establish technical plans, divide work across the team, manage execution risks, and drive multiple model-enablement initiatives from initial investigation to production delivery.
• Participate in and lead architecture reviews, design reviews, code reviews, and performance and accuracy reviews.
• Mentor engineers, develop technical expertise within the team, and establish engineering practices for maintainable and production-quality AI software.
• Communicate technical strategy, project status, dependencies, and risks to engineering leadership and cross-functional stakeholders.