AI Machine Learning Developer

R Systems is seeking a talented Edge AI Machine Learning Engineer with specialized expertise in embedded GPU/NPU acceleration to join our team. The ideal candidate will have extensive hands-on experience in developing and optimizing AI inference models for embedded GPU/NPU architectures. As a Machine Learning Engineer specializing in Edge AI, you will play a crucial role in shaping the future Edge AI solution, leveraging the power of GPU/NPU acceleration and enterprise-grade, large-scale edge computing. If you are a skilled Edge AI Machine Learning Engineer with a passion for pushing the boundaries of edge computing and GPU/NPU acceleration, we want to hear from you! Apply now to be part of our dynamic and collaborative team and join us in shaping the future of AI at the edge and revolutionizing industries with innovative Edge AI solutions! Your Contribution Develop and optimize AI inference models for deployment on edge devices with embedded GPU/NPU accelerators, focusing on low-latency inference runtimes. Implement and fine-tune low-latency inference pipelines to meet real-time performance requirements. Collaborate with cross-functional teams to integrate AI inference solutions into edge computing platforms and applications. Conduct performance profiling and optimization to maximize the efficiency of GPU/NPU acceleration for Edge AI inference. Influence the Edge AI strategy by providing expert advice on design and architecture Stay current with advancements in GPU, NPU, and Edge AI frameworks, incorporating them into solution designs as appropriate. Provide technical expertise and support to project teams, ensuring successful implementation and deployment of Edge AI solutions. Basic Qualifications Bachelor’s degree in computer science, Engineering, or a related field; Master’s degree preferred. 5+ years of hands-on experience in AI model development and deployment, with a focus on edge computing and inference runtime optimization. Experience in developing full Edge / On Prem AI Application based on: NLP, Time Series (Sensors) processing, etc. Python strong programming skills in languages. Optional: C/C++ Proficiency in ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, XGBoost) with focus on edge AI applications deployment (e.g., Glow, TFLite, TensorRT) Experience with MLOps frameworks (e.g., Kubeflow, MLflow, TFX, Airflow, H2O) Extensive experience with GPU/NPU acceleration for AI inference, including optimization techniques (tensor, pipeline, data, sharded data parallelism) and performance tuning, Hands on experience with one or more GPU/NPU frameworks: CUDA, Vulkan, OpenCL, familiarity with NVIDIA Jetson, ARM Mali, or relevant SoC configurations. Knowledge of parallel computation, memory scheduling, and structural optimization Excellent problem-solving and analytical skills, with a passion for innovation and continuous learning. Additional Skills (Preferred) Experience with edge device hardware and software integration. Familiarity with edge computing architectures and IoT platforms. Experience with edge AI applications in domains such as robotics, autonomous vehicles, or industrial automation