Senior Staff Software Engineer, AI Model Enablement & Software Tools
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Software Engineering
In short
As a Senior Staff Software Engineer, you will lead the enablement of state-of-the-art AI models like LLMs and LVMs on Snapdragon platforms. This role involves technical leadership in analyzing, optimizing, and compiling modern AI architectures, working at the intersection of model architecture, ML algorithms, and hardware-aware optimization. You will also lead a small team while contributing as an individual.
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, 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, and configuration schemas.
- Define scalable approaches for model re-authoring, graph adaptation, graph lowering, decomposition, fusion, and backend-specific optimization.
- Architect reusable model-enablement solutions instead of model-specific workarounds.
- Lead the design of declarative and extensible pipeline capabilities, including reusable stages, model mappings, and quantization recipes.
- 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, and calibration.
- Evaluate accuracy, latency, throughput, memory usage, model size, and power efficiency.
- Guide enablement of advanced Generative AI features such as KV-cache optimization and speculative decoding.
- Debug complex issues across model architecture, PyTorch and ONNX graphs, quantization, compiler, runtime, and hardware layers.
- Establish technical plans, divide work across the team, manage execution risks, and drive multiple model-enablement initiatives.
- 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.
- Communicate technical strategy, project status, dependencies, and risks to engineering leadership and cross-functional stakeholders.
Requirements
- 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.
- 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, and normalization.
- Strong understanding of quantization and calibration concepts, including PTQ, QAT, mixed precision, and weight and activation quantization.
- Strong knowledge of computational graphs, graph transformations, operator decomposition, 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.
- 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.
Desired 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.
Benefits
- Competitive annual discretionary bonus program
- Opportunity for annual RSU grants
- Highly competitive benefits package designed to support your success at work, at home, and at play
- Recruiter will discuss all that Qualcomm has to offer
- Review more details about US benefits at this link
Skills
PythonC++PyTorchONNX
#AI#Machine Learning#Software Engineering#LLM#Model Optimization#Compiler
About Qualcomm
Enabling a world where everyone and everything can be intelligently connected.
Enabling a world where everyone and everything can be intelligently connected.\n\n
Company
QualcommJob Posted
6 days ago
Employment Type
Full Time
Work mode
On Site
Salary
US$194,400–291,600 / year
Experience Level
Senior
Locations
San Diego, United States of America
Qualification
Bachelor, Master, Doctoral
Applicants
Be an early applicant
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