Embedded Edge AI/ML Engineer

HP; Spring (TX); On-site; USD 147,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program

Flexible spending account

Life insurance

Parental leave

Holidays

Vacation and sick leave

Job summary

HP seeks an Embedded AI/ML Developer to design and optimize AI-enabled embedded software for HP’s commercial PC and connected device portfolio. You will deploy efficient models at the edge, integrate AI with firmware, and enhance user experiences on resource-constrained platforms.

Responsibilities include model optimization, runtime integration, performance tuning, debugging, and keeping pace with edge AI technologies.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related technical discipline.
• 7–10 years of embedded software, firmware, AI/ML deployment, edge inference, or system-level software development.
• Proficiency in C/C++ and Python with experience in prototyping and testing embedded AI/ML workloads.

Responsibilities

• Design, develop, and optimize embedded AI/ML software for edge devices and HP platforms.
• Convert AI/ML concepts into production-ready embedded implementations optimized for latency, memory, and power constraints.
• Integrate AI inference engines and accelerators into firmware and system software environments.
• Collaborate with cross-functional teams on AI feature requirements, architecture, and validation plans.
• Profile and tune AI workloads to improve inference performance and reduce memory footprint.

Skills

C/C++

Python

Embedded systems

AI/ML inference

Performance optimization

Debugging

Hardware awareness

Education

Bachelor’s or Master’s in CS/CE/AI-related

Tools

TensorRT

ONNX

OpenVINO

TensorFlow Lite

PyTorch

Linux/RTOS

Job description

HP seeks an Embedded AI/ML Developer to design and optimize AI-enabled embedded software for HP’s commercial PC and connected device portfolio. You will deploy efficient models at the edge, integrate AI with firmware, and enhance user experiences on resource-constrained platforms.

Responsibilities include model optimization, runtime integration, performance tuning, debugging, and keeping pace with edge AI technologies.