Company Description At embedUR systems, we accelerate Edge AI development by delivering high-performance AI for resource-constrained devices. We offer full-stack Edge AI services, including model optimization, firmware integration, hardware bring-up, and platform tuning, enabling rapid deployment of intelligent devices. To further this, we provide ModelNova—a library of 100+ pre-trained, production-ready Edge AI models optimized for small chips. With deep roots in embedded software, networking, and telecom, and a reputation for reliability, we support both startups and Fortune 500 companies. Headquartered in Silicon Valley, our global team of engineers is dedicated to expert execution in challenging scenarios. Role Description This is a full-time, on-site role for a Senior Machine Learning Engineer (Edge AI) located in the Greater Chennai Area. The Senior Machine Learning Engineer will be responsible for developing and optimizing machine learning models, integrating AI models with firmware and hardware, and collaborating with cross-functional teams to bring Edge AI solutions from concept to production. The role involves deploying and tuning models on resource-constrained devices, conducting extensive testing, and ensuring robust performance across various Edge AI applications. Qualifications Proficiency in Pattern Recognition, Neural Networks, and Statistics Strong foundation in Computer Science and Algorithms Experience with model optimization and deploying AI on resource-constrained devices Excellent programming skills in Python, C++, or similar languages Ability to work collaboratively in cross-functional teams Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field
We’re seeking a Senior Machine Learning Engineer with deep expertise in building and deploying ML models optimized for edge devices. This role is ideal for someone passionate about pushing the boundaries of AI on resource-constrained hardware, across domains such as computer vision, audio analytics, and sensor-based signal processing. Key Responsibilities Design, train, and optimize deep learning models for edge AI use cases: Computer vision (e.g., object detection, image classification, segmentation) Audio/speech processing (e.g., wake word detection, denoising) Signal processing across multimodal sensor data Develop scalable and efficient model training and evaluation pipelines. Quantize, prune, and compress models for real-time inference on NPUs, DSPs, and microcontrollers. Conduct applied research and benchmark new model architectures or techniques for edge deployment. Prepare and curate training/validation datasets using best practices for data quality and balance. Collaborate with embedded software engineers to integrate models into edge production environments. Monitor model performance, perform error analysis, and iterate for continuous improvement. Document model architectures, assumptions, and deployment requirements for stakeholders. Qualifications Master’s degree (or equivalent experience) in Computer Science, Machine Learning, or a related discipline. 5+ years of hands-on experience in developing, optimizing, and deploying ML models in production. Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, ONNX , and tools like OpenCV , Ultralytics , or equivalent. Demonstrated experience with edge inference optimization : Model quantization and compression Deployment on NPUs , DSPs , or microcontrollers Familiarity with ARM Ethos-U/Vela , TensorRT , TFLite , or similar edge AI compilers is a strong plus. Solid understanding of signal processing for image, audio, and other sensor modalities. Experience with cloud-based training pipelines (AWS, GCP, or Azure) is a plus. Strong analytical and problem-solving skills, with a track record of innovation. Excellent communication and documentation skills.