Edge AI Machine Learning Engineer
5 godzin temu
R Systems is seeking a highly skilled Edge AI Machine Learning Engineer to join our team. As a key member of our Edge AI team, you will be responsible for developing and optimizing AI inference models for deployment on edge devices with embedded GPU/NPU accelerators.
Key Responsibilities:- Design and develop AI inference models for edge devices with embedded GPU/NPU accelerators.
- Optimize AI inference models for 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.
- Stay current with advancements in GPU, NPU, and Edge AI frameworks, incorporating them into solution designs as appropriate.
- 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.
- Strong programming skills in languages such as Python and 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.
R Systems is a global technology and analytics services company with a presence in Europe. We help our clients achieve speed-to-market, overcome digital barriers, and create business value with our specialized service offerings and consultative business approach. We speak the language of business as fluently as we do the language of technology. In other words: We speak Digital. Our goal: accelerate our clients' digital leadership.
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