Job Information
Qualcomm Incorporated Physical AI Model Optimization Lead - Qualcomm Advanced Robotics Team in Santa Clara, California
General Summary:
*Hiring in San Diego and Santa Clara
About Qualcomm Robotics
Qualcomms Advanced Robotics Team is building an AI-first stack and platform for the next generation of general-purpose robotsfrom AMRs and cobots to emerging humanoidsby pairing heterogeneous compute (CPU/GPU/DSP/NPU) with a full Robotics SDK and developer tooling for manipulation, perception, navigation, and fleet workflows. The team leverages Qualcomms success in automated driving, advanced end-to-end AI development, and safety architecture to accelerate growth in this emerging market.
Role Overview
The Physical AI Model Optimization Lead will drive the technical execution of advanced robotic AI model deployment on Qualcomm Dragonwing chipsets. This is a deeply technical, hands-on role focused on quantization, compression, optimization, mixed-precision tuning, and hardware-aware graph transformations using Qualcomms internal toolchains.
This role provides exposure to industry-leading robotics-centric AI models, including next-generation vision-language-action (VLA) architectures and complex multimodal transformers, with responsibility for taking models from research grade to highly optimized real-time deployment on heterogeneous compute.
Your work will directly impact real robotsand the teams building them.
Why Join Us
Shape the core platform that powers intelligent, safe, and scalable robotic operations.
Work with some of the most advanced robotic AI models in the world.
Influence the optimization and deployment pipeline for next-generation robotic intelligence.
Access competitive compensation, deep technical growth, and opportunities to shape the future of on-device AI.
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.
Preferred Qualifications:
MS in Computer Science, Electrical Engineering, Robotics, or a related field; PhD a plus.
5+ years of experience in embedded/on-device AI, model optimization, or performance engineering.
Deep technical expertise in:
Mixed-precision quantization (INT8/FP16/FP8)
QDQ graph-based quantization flows
PTQ and QAT workflows
Model compression techniques (pruning, distillation, low-rank methods)
Strong experience with ONNX and PyTorch or TensorFlow model export and graph manipulation.
Hands-on profiling experience on edge devices, custom SoCs, or heterogeneous compute targets.
Experience with Qualcomm toolchains: AI Hub Workbench, AIMET, QNN, QGenie, or similar.
Background optimizing transformer-based perception, VLMs, and VLA architectures.
Understanding of heterogeneous compute system design and operator scheduling.
Direct experience supporting customers or partners in model deployment and performance tuning.
Responsibilities
Execute end-to-end model optimization, including graph rewrites, operator fusion, and hardware-specific transformations.
Apply mixed-precision quantization and QDQ workflows (PTQ/QAT) for high-performance deployment.
Implement compression techniques such as pruning, distillation, and low-rank factorization.
Debug accuracy issues using fine-grained tensor comparisons during quantization and conversion.
Use Qualcomm tools (AI Workbench, AIMET, QNN, QGenie, profiler ) to convert, validate, and optimize models.
Map and tune models across heterogeneous compute (DSP/NPU/GPU), including operator placement and kernel selection.
Perform detailed performance profiling and analyze memory, tiling, and scheduling behavior.
Collaborate with internal teams and external customers to integrate, tune, and validate models on Dragonwing hardware.
How Youll Lead
Set the technical bar for optimization of physical AI models.
Own optimization workflows from initial model drop ? compression ? mixed-precision/QDQ quantization ? conversion ? on-device profiling ? final tuned deployment.
Work closely with Qualcomms existing tools and teamsAI Hub Workbench, QNN, AIMET, QGenie, compiler, and robotics AI.
Serve as the technical authority on quantization correctness, mixed-precision design, and hardware-aware optimization for physical AI.
Drive improvements in internal tools and processes through hands-on experiments and data-driven reporting.
Why Qualcomm
Gain direct access to state-of-the-art robotic AI models and run them on advanced heterogeneous compute.
Work at the intersection of embedded AI, robotics, and high-performance model optimization.
Collaborate with te