Embedded AI/ML Developer

HP•Spring, TX

3d•$147,050 - $230,850

About The Position

The Embedded AI/ML Developer will design, develop, and optimize AI-enabled embedded software solutions for HP’s commercial PC and connected device portfolio. This role focuses on deploying efficient machine learning models at the edge, integrating AI capabilities with firmware and system software, and enabling intelligent user experiences across resource-constrained platforms. The engineer will work closely with hardware, firmware, software, and data science teams to translate AI/ML concepts into production-ready embedded implementations. Responsibilities include model optimization, inference runtime integration, performance tuning, debugging, documentation, and staying current with emerging edge AI technologies, tools, and industry best practices.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Statistics, Mathematics, Artificial Intelligence, 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.
Proficient in C/C++ and Python; familiar with embedded scripting, automation, build systems, and model deployment workflows.
Experience with AI/ML frameworks and formats such as PyTorch, TensorFlow, ONNX, TensorFlow Lite, OpenVINO, or similar toolchains.
Knowledge of embedded system architecture, boot flow, firmware interfaces, memory constraints, power management, and real-time execution tradeoffs.
Skilled in embedded debugging and profiling using JTAG, SWD, logic analyzers, oscilloscopes, performance counters, tracing tools, or vendor-specific debug environments.
Experience optimizing AI workloads for latency, throughput, memory footprint, thermal behavior, and battery life on constrained devices.
Familiarity with AI accelerator SDKs, NPU/DSP/GPU offload, heterogeneous compute, and hardware/software co-optimization.
Understanding of RTOS concepts, Linux/Windows system software, multi-threaded development, secure update mechanisms, and production-quality embedded software practices.
Strong analytical and problem-solving skills with the ability to debug complex interactions across model behavior, firmware, drivers, sensors, and host software.
Ability to work independently and collaboratively in a cross-functional engineering environment, translating AI concepts into reliable product experiences.

Nice To Haves

Embedded AI/ML Engineering
Hands-on experience deploying AI/ML models on embedded systems, edge devices, MCUs, SoCs, NPUs, DSPs, or other constrained compute platforms.
Experience with model optimization techniques such as quantization, pruning, compression, TensorRT, ONNX, TFLite, or similar deployment toolchains.
Strong understanding of ML inference pipelines, data preprocessing, sensor input handling, feature extraction, and runtime performance tuning.
Experience integrating AI workloads with embedded firmware, device drivers, RTOS, Linux, Windows, or low-level system software environments.
Familiarity with AI validation, automated testing, CI/CD pipelines, model benchmarking, and regression testing for embedded platforms.
Embedded Systems and Platform Integration
Strong embedded software development experience using C/C++ and Python for prototyping, automation, model conversion, and testing.
Experience working with sensors, camera/audio pipelines, telemetry data, wireless modules, or contextual signals used by AI-enabled experiences.
Knowledge of embedded communication protocols such as UART, I2C, SPI, USB, PCIe, Bluetooth, Wi-Fi, or other device interconnect standards.
Ability to read hardware specifications, device datasheets, schematics, and platform architecture documents to support AI/ML feature integration.

Responsibilities

Designs, develops, and optimizes embedded AI/ML software for edge devices, including PCs, docking solutions, displays, peripherals, and other intelligent client platforms.
Converts AI/ML algorithms and proof-of-concept models into efficient, production-quality embedded implementations optimized for latency, memory, power, and compute constraints.
Integrates machine learning inference engines, model runtimes, and AI accelerators into embedded firmware and system software environments.
Collaborates with cross-functional teams to define AI feature requirements, system architecture, data flow, model deployment strategy, and validation plans.
Profiles and tunes embedded AI workloads to improve inference performance, reduce memory footprint, improve responsiveness, and optimize power consumption.
Develops and maintains software interfaces between AI/ML components, firmware, device drivers, sensors, embedded controllers, and host applications.
Supports model compression, quantization, pruning, benchmarking, and deployment using embedded AI frameworks and hardware acceleration technologies.
Troubleshoots complex system-level issues involving AI inference, firmware behavior, sensor data, device communication, and platform integration.
Creates and maintains technical documentation, including architecture descriptions, design specifications, model deployment guides, validation procedures, and integration notes.
Explores emerging embedded AI, TinyML, NPU, MCU, sensor fusion, and edge inference technologies to help drive innovation across future HP platforms.