This role pays less than
52%
of similar roles. Most pay
$156,933–$216,087
— the shaded band above.
At the midpoint, this role pays about
$189k
versus about
$187k
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
104 open roles
on FindRole.
Listed pay typically runs
$130,700–$205,200
across 90 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, displays, and peripherals. The engineer will convert proof-of-concept models into production-ready implementations while performing model optimization through quantization, pruning, and compression. Key responsibilities include profiling workloads for latency and power, managing inference engines, and troubleshooting complex system-level issues involving sensor data and device communication. Required skills include proficiency in C/C++ and Python, experience with PyTorch, TensorFlow, ONNX, and TFLite, and knowledge of hardware acceleration via NPUs or DSPs. The role addresses the challenge of delivering intelligent user experiences on constrained hardware.
Design 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, power, and compute constraints.
Integrate inference engines, model runtimes, and hardware accelerators into firmware and system software environments.
Perform model compression, quantization, pruning, and benchmarking using embedded AI frameworks and acceleration technologies.
Profile and tune workloads to improve inference performance and reduce the memory footprint on resource-constrained devices.
Develop software interfaces between AI components, device drivers, sensors, and host applications.
Troubleshoot complex system-level issues involving AI inference, firmware behavior, 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, TensorFlow Lite, or OpenVINO.
Experience deploying models on constrained platforms like MCUs, SoCs, NPUs, DSPs, or other edge devices (preferred).
Experience with model optimization techniques including quantization, pruning, compression, TensorRT, and similar deployment toolchains (preferred).
Knowledge of embedded communication protocols such as UART, I2C, SPI, USB, PCIe, Bluetooth, and Wi-Fi.
Skilled in using hardware debugging tools like JTAG, SWD, logic analyzers, oscilloscopes, and performance counters.
LLMs
RAG
PyTorch
TensorFlow
Hugging Face
Python
C++
Rust
Go
TypeScript
React
Angular
AWS
Azure
Google Cloud
CI/CD
ONNX Runtime
OpenVINO
TensorRT
WebRTC
gRPC
Windows Development
Computer Vision
Speech Synthesis
Embedded Systems
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