This role pays more than
50%
of similar roles. Most pay
$162,000–$217,700
— the shaded band above.
At the midpoint, this role pays about
$189k
versus about
$190k
for comparable roles.
Based on 240 similar postings.
Employer
About HP Inc.
HP Inc. is a global technology company that develops and sells personal computers, printers, and related supplies and services. Its products include laptops, desktops, workstations, and printing solutions for consumers and businesses worldwide.
HP Inc. currently has
92 open roles
on FindRole.
Listed pay typically runs
$147,050–$216,900
across 79 roles with salary data.
The Embedded AI/ML Developer joins a cross-functional team to design, develop, and optimize AI-enabled embedded software for a portfolio of commercial PCs and connected devices. This role focuses on deploying efficient machine learning models at the edge by integrating AI capabilities with firmware and system software across resource-constrained platforms like docking solutions and peripherals. The engineer will convert proof-of-concept models into production-ready implementations, focusing on performance tuning for latency, memory, power, and compute constraints. Key responsibilities include model compression, quantization, pruning, and managing inference runtimes. Required skills include proficiency in C/C++ and Python, along with experience using frameworks like PyTorch, TensorFlow, ONNX, and TFLite. The role involves solving complex technical challenges involving sensor fusion, NPU acceleration, and hardware-software co-optimization to deliver intelligent user experiences on various embedded systems and mobile platforms.
Develop and optimize AI-enabled embedded software for PCs, docking solutions, and other intelligent client platforms.
Convert machine learning algorithms into production-ready implementations optimized for latency, memory, and power constraints.
Integrate inference engines, model runtimes, and hardware accelerators into firmware and system software environments.
Perform model compression, quantization, and pruning using embedded AI frameworks and acceleration technologies.
Profile and tune workloads to improve performance, reduce memory footprint, and optimize power consumption.
Develop software interfaces between AI components, device drivers, sensors, and host applications.
Troubleshoot complex system-level issues involving inference behavior, firmware, and sensor data integration.
Create technical documentation including architecture descriptions, design specifications, and model deployment guides.
What we're looking for
Bachelor's or Master's degree in Computer Science, Computer Engineering, Statistics, Mathematics, AI, Machine Learning, Robotics, or a related technical discipline.
7–10 years of relevant experience in embedded software, firmware, AI/ML deployment, edge inference, or system-level software development.
Proficiency in C/C++ and Python for embedded scripting, automation, and model conversion.
Experience with AI/ML frameworks and formats such as PyTorch, TensorFlow, ONNX, TFLite, or OpenVINO.
Expertise in optimizing AI workloads for latency, memory, power, and thermal constraints on resource-constrained devices.
Knowledge of embedded communication protocols like UART, I2C, SPI, USB, PCIe, Bluetooth, and Wi-Fi.
Experience with model optimization techniques such as quantization, pruning, compression, TensorRT, or similar deployment toolchains (preferred).
Familiarity with AI validation, automated testing, CI/CD pipelines, and hardware debugging tools like JTAG or logic analyzers (preferred).
C++
Python
TensorRT
ONNX
TFLite
OpenVINO
PyTorch
TensorFlow
Embedded Systems
Firmware
Linux
Windows
RTOS
CI/CD
NPU
DSP
MCU
I2C
SPI
UART
USB
PCIe
Quantization
Pruning
Model Compression
Machine Learning
C++
C
Python
Core ML
TensorFlow Lite
ONNX Runtime
Embedded Systems
RTOS
Microcontrollers
ASIC
Quantization
Pruning
Computer Vision
NLP
Audio Processing
Linux
Python
Java
C++
MLOps
DevOps
Natural Language Processing
Deep Learning
Computer Vision
Docker
Kubernetes
Infrastructure-as-Code
AWS
Azure
Linux
UNIX
Cloud-native
Data Science
Machine Learning Engineering