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Sr. Software Engineer, AI Software Tools; Onsite

Job in San Diego, San Diego County, California, 92189, USA
Listing for: Qualcomm
Full Time position
Listed on 2026-09-12
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Engineer, Machine Learning/ ML Engineer, Software Architect
Salary/Wage Range or Industry Benchmark: 158400 - 237600 USD Yearly USD 158400.00 237600.00 YEAR
Job Description & How to Apply Below

Company

Qualcomm Technologies, Inc.

Job Area

Engineering Group, Engineering Group >
Machine Learning Engineering

Job Description

This is a full‑time onsite role requiring five days a week in the office at Qualcomm’s San Diego location. As a Staff or Senior Staff Software Engineer on the Qualcomm AI Stack SDK Software team, you will design, develop, and deliver advanced AI/ML software solutions for generative AI inference on Snapdragon platforms. The role focuses on model optimization, quantization, graph transformations, and runtime execution for modern AI architectures, including LLMs, LVMs, and LMMs.

You will work at the intersection of machine learning algorithms, inference optimization, graph lowering, and systems software, contributing directly to the Qualcomm AI Stack SDK (QAIRT) and associated tools, such as delegates support for ONNX Runtime, Execu Torch, and TFLite/LiteRT frameworks. Collaboration with engineers across multiple teams—ML Research, AI accelerator HW/SW, Product Management, Program Management, and QA—is central to driving features from concept to production.

This position requires strong technical ownership, independent work, and the capability to deliver features end‑to‑end while mentoring junior engineers.

Responsibilities
  • Convert, optimize, and deploy AI models from PyTorch and ONNX frameworks for efficient inference on Snapdragon platforms.
  • Design and implement graph transformations, graph lowering, and optimization techniques within AI runtime environments such as ONNX Runtime, Execu Torch, and Qualcomm AI Stack SDK.
  • Apply knowledge of quantization and performance optimization to improve latency, throughput, memory usage, and power efficiency.
  • Work at the forefront of generative AI, understanding advanced algorithms such as attention mechanisms, mixture‑of‑experts (MoE), low‑rank adapter (LoRA), and emerging inference optimization techniques (e.g., speculative decoding).
  • Collaborate with ML Research teams to prototype and productize new features and techniques into SDK solutions.
  • Debug complex issues across models, runtime, OS, compiler, and hardware layers, working closely with QA and customer teams.
  • Design, implement, and deliver new features and enhancements to the Qualcomm AI Stack SDK.
  • Participate in design reviews and code reviews, ensuring software quality and maintainability.
  • Mentor junior engineers, helping them prioritize work and drive execution across multiple initiatives.
Minimum Qualifications
  • Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of hardware engineering, software engineering, systems engineering, or related experience.
  • Master’s degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of hardware engineering, software engineering, systems engineering, or related experience.
  • PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of hardware engineering, software engineering, systems engineering, or related experience.
  • Bachelor’s degree in computer science, computer engineering, or related field and 6+ years (Staff) or 8+ years (Senior Staff) of experience in software design, development, and delivery.
  • Master’s degree or PhD in computer science, computer engineering, or related field and 5+ years (Staff) or 7+ years (Senior Staff) of experience in software design, development, and delivery.
  • At least 3+ years of hands‑on experience in AI/ML software development, focusing on inference or model optimization.
  • Strong understanding of AI/ML fundamentals, including deep learning and inference pipelines.
  • Deep understanding of transformer architectures, attention mechanisms, and performance trade‑offs.
  • Proficiency in Python and C/C++ for…
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