Physical AI Model Optimization Engineer – Qualcomm Advanced Robotics Team

Company:
Qualcomm Technologies, Inc.

Job Area:
Engineering Group, Engineering Group >
Machine Learning Engineering

General

Summary:

Qualcomm Advanced Robotics Team is building the AI first stack and platform for the next generation general purpose robots – from AMRs and cobots to emerging humanoids by pairing heterogeneous compute (CPU/GPU/DSP/NPU) with a full Robotics SDK and developer tooling for manipulation, perception, navigation, and fleet workflows. We are leveraging our success in automated driving product portfolio, advanced AI end to end development and tools, and safety architecture to accelerate the growth in this emerging market.

We are seeking a Physical AI Model Optimization Engineer to help bring cutting-edge robotic AI models onto Qualcomm Dragon wing chipsets using Qualcomm’s internal deployment and optimization tool chains. This role is highly execution-focused and centers on applying existing Qualcomm tools, workflows, and compilers to onboard, optimize, validate, and deploy advanced AI models for real-time robotic systems.

A key responsibility of this team is creating and maintaining a curated library of robotics-focused AI models that are pre-optimized for deployment on Qualcomm chips. These models-spanning perception, control, VLA, and multimodal reasoning-will be packaged, validated, and made available for customers as high-performance, deploy-ready components that accelerate their development cycles.

You’ll work hands‑on with next-generation models transforming research architectures into reliable, hardware‑efficient implementations. You will work directly with Qualcomm’s AI Stack and contribute targeted fixes, enhancements, or feature requests that improve Qualcomm’s internal pipelines for robotics‑focused usecases.

Your work will directly impact real robots‑and the teams building them.

• Use Qualcomm’s internal AI tool chains to onboard, convert, and optimize large‑scale research models for Dragon wing deployment.
• Apply Qualcomm‑supported quantization, compression, and mixed‑precision workflows to meet latency, memory, and power constraints.
• Execute hardware‑aware graph transformations and operator adjustments using QC‑provided graph tools and compilers.
• Profile model performance across heterogeneous compute (NPU/DSP/GPU/CPU) using Qualcomm profiling utilities and diagnose optimization opportunities.
• Validate accuracy, stability, and runtime behavior of quantized and optimized models on real robotic hardware.
• Build automation, scripts, and reproducible processes around Qualcomm’s tool chains to accelerate onboarding throughput.
• Provide bug reports, patches, or minor contributions back to Qualcomm tools where needed to support model deployment, but not as a primary responsibility.
• Work closely with platform and robotics engineering teams to integrate optimized models into production systems.

• 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 teams building Qualcomm’s inference engines, compilers, and silicon.
• Ship improvements that immediately affect real robots across industries.

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 work experience.
OR
Master’s degree in Computer Science, Engineering, Information Systems, or related field and 3 years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2 years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

• 3 years of experience in embedded or on‑device AI, model optimization, or performance engineering.
• Strong hands‑on experience applying quantization (PTQ/QAT), pruning, compression, mixed‑precision tuning, and model transformation techniques.
• Experience using vendor‑specific AI…