Senior Staff Software Engineer, AI Model Enablement & Software Tools

Originally posted 15 October 2026 by the employer.

About the role

This involves leading 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).

What you'll do

• 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.
• 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.
• 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.
• Debug complex issues across model architecture, PyTorch and ONNX graphs, quantization, compiler, runtime, operating system, and hardware layers.

What you'll need

• 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.
• 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.

Nice to have

• 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.
• 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.
• 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.

Skills: AI software stack, model enablement, quantization, compiler, runtime technologies, LLM