Embedded AI/ML Developer

Design, develop, and optimize embedded AI/ML software for edge devices, including PCs, docking solutions, displays, peripherals, and other intelligent client platforms Convert AI/ML algorithms and proof-of-concept models into efficient, production-quality embedded implementations optimized for latency, memory, power, and compute constraints Integrate machine learning inference engines, model runtimes, and AI accelerators into embedded firmware and system software environments Collaborate with hardware, firmware, software, and data science teams to define AI feature requirements, system architecture, data flow, model deployment strategy, and validation plans Profile and tune embedded AI workloads to improve inference performance, reduce memory footprint, improve responsiveness, and optimize power consumption Develop and maintain software interfaces between AI/ML components, firmware, device drivers, sensors, embedded controllers, and host applications Support model compression, quantization, pruning, benchmarking, and deployment using embedded AI frameworks and hardware acceleration technologies Troubleshoot complex system-level issues involving AI inference, firmware behaviour, sensor data, device communication, and platform integration Create and maintain technical documentation, including architecture descriptions, design specifications, model deployment guides, validation procedures, and integration notes Explore emerging embedded AI, TinyML, NPU, MCU, sensor fusion, and edge inference technologies to drive innovation across future HP platforms 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 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 Strong embedded software development experience using C/C++ and Python Experience working with sensors, camera/audio pipelines, telemetry data, wireless modules, or contextual signals Knowledge of embedded communication protocols such as UART, I2C, SPI, USB, PCIe, Bluetooth, and Wi‑Fi Ability to read hardware specifications, device datasheets, schematics, and platform architecture documents Proficiency in C/C++ and Python Experience with AI/ML frameworks and formats such as PyTorch, TensorFlow, ONNX, TensorFlow Lite, and OpenVINO 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 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 Ability to work independently and collaboratively in a cross‑functional engineering environment Core Competencies Demonstrates expertise in designing and optimizing embedded AI/ML software for edge devices, with a strong focus on model deployment, performance tuning, and integration with hardware and firmware. Proficient in utilizing AI frameworks and optimization techniques to enhance system efficiency and responsiveness. Highest-signal resume keywords Embedded Software Development AI/ML Model Deployment C/C++ and Python Proficiency Model Optimization Techniques Integration with Embedded Firmware ATS Optimization Keywords Hard Skills Embedded Software Development AI/ML Model Deployment C/C++ Programming Python Programming Model Optimization Techniques AI/ML Frameworks Embedded System Architecture Debugging and Profiling Machine Learning Inference Pipelines Sensor Input Handling Soft Skills Collaboration Problem‑Solving Independent Work Industry Keywords Embedded AI Edge Inference AI Validation Model Benchmarking Firmware Interfaces Communication Protocols Hardware/Software Co‑Optimization Multi‑Threaded Development Real‑Time Execution Cross‑Functional Engineering Tools & Technologies TensorRT ONNX TFLite PyTorch TensorFlow OpenVINO RTOS Linux Windows JTAG #J-18808-Ljbffr Jobtailor