Physical AI Model Optimization Lead - Advanced Robotics Team

Position: Physical AI Model Optimization Lead - Qualcomm Advanced Robotics Team
Company:Qualcomm Technologies, Inc.
Job Area:Engineering Group, Engineering Group >
Machine Learning Engineering

General

Summary:

*** Hiring in San Diego and Santa Clara

About Qualcomm Robotics

Qualcomm’s Advanced Robotics Team is building an AI‑first stack and platform for the next generation of 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. The team leverages Qualcomm’s 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 chipsets. This is a deeply technical, hands‑on role focused on quantization, compression, optimization, mixed‑precision tuning, and hardware‑aware graph transformations using Qualcomm’s internal tool chains.

A key responsibility of this role 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.

This role provides exposure to industry‑leading robotics‑centric AI models, including next‑generation vision‑language‑action (VLA) architectures and complex multimodal transformers and reasoning models, with responsibility for taking models from research‑grade to highly optimized real‑time deployment on heterogeneous compute.

Your work will directly impact real robots—and 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 Tensor Flow model export and graph manipulation.

• Hands‑on profiling experience on edge devices, custom SoCs, or heterogeneous compute targets.

• Experience with Qualcomm tool chains: 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…