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.

Minimum Qualifications (Must Have)

·     Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field and 8+ years of experience in software design, development, and delivery.

OR

·     Master’s degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field and 6+ years of experience in software design, development, and delivery.

·     5+ years of hands-on experience in AI/ML software development, model inference, model optimization, compiler technologies, or related systems software.

·     Strong knowledge of modern AI model architectures, including transformer-based LLMs, vision models, and multimodal or vision-language models.

·     Deep understanding of transformer internals, including attention, positional encoding, KV cache, prefill and decode execution, normalization, feed-forward networks, and emerging architecture patterns.

·     Strong understanding of quantization and calibration concepts, including PTQ, QAT, mixed precision, weight and activation quantization, encoding generation, and accuracy-performance tradeoffs.

·     Strong knowledge of computational graphs, graph transformations, operator decomposition, graph lowering, layout and shape transformations, and backend-aware optimization.

·     Excellent software architecture and system-design skills, with demonstrated experience designing scalable, modular, extensible, and maintainable frameworks, APIs, and production software systems.

·     Proficiency in Python and C/C++ for production-quality software development.

·     Strong experience with PyTorch and ONNX model representations and tooling.

·     Demonstrated proficiency with agentic coding tools such as Claude Code, OpenAI Codex, or equivalent tools for codebase exploration, design, implementation, testing, debugging, and code review. Candidates must apply strong engineering judgment and validate AI-generated solutions.

·     Demonstrated ability to investigate unfamiliar model architectures, identify software and backend gaps, and develop scalable technical solutions.

·     Strong debugging and root-cause-analysis skills across model, framework, compiler, runtime, and hardware boundaries.

·     Demonstrated technical leadership, including leading a small team, mentoring engineers, prioritizing work, and driving complex projects end-to-end.

·     Ability to work independently and collaborate effectively with research, hardware, software, product, QA, and customer-facing organizations.

Preferred Qualifications

·     Experience bringing up LLM, LVM, VLM, or multimodal models on embedded, edge, mobile, or accelerator-based platforms.

·     Experience designing model-enablement pipelines or frameworks for model loading, re-authoring, quantization, export, compilation, deployment, and execution.

·     Experience with AIMET or equivalent model quantization and optimization frameworks.

·     Experience with Hugging Face Transformers, model repositories, tokenizers, processors, PEFT, and related ecosystem components.

·     Knowledge of advanced quantization techniques such as blockwise or groupwise quantization, weight rotation, adaptive scaling, sequential MSE optimization, and quantizer-placement optimization.

·     Experience with graph optimization and compiler technologies such as ONNX Runtime, ExecuTorch, TFLite/LiteRT, MLIR, or equivalent compiler and runtime frameworks.

·     Experience with LoRA, MoE, speculative decoding, hybrid attention architectures, state-space or recurrent model components, and vision encoder/decoder models.

·     Understanding of accelerator architecture, operator support, memory hierarchy, data movement, precision support, and hardware/software performance tradeoffs.

·     Experience with Android and/or RTOS environments such as QNX.

·     Experience with Linux development, CMake-based build environments, agile software development, and Git-based source control.

·     Experience interacting with senior leadership and communicating technical strategy, execution status, and project risks.

·     Previous experience leading and mentoring a globally distributed engineering team.

·     User-level or development experience with commercial AI software stacks and model-enablement frameworks, including model conversion, optimization, compilation, deployment, and runtime tools.

·     Exposure to Snapdragon SoCs and AI accelerators such as NPUs or HTPs.

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Pay range and Other Compensation & Benefits:

$194,400.00 - $291,600.00

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 at Qualcomm.  We 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 at play. Your 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.

If you would like more information about this role, please contact Qualcomm Careers.