Q - Machine Learning Researcher – Edge & Efficient AI

Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life. We are looking for a talented and motivated Machine Learning Engineer to join our team. You will work within a collaborative, research-driven engineering culture that values innovation and rigor, with the opportunity to build impactful AI products deployed at scale on real devices. We offer competitive compensation, benefits, and opportunities for professional growth. Join our team as a Machine Learning Engineer and help shape the future of on-device AI. You'll research, design, and deploy cutting-edge deep learning models optimized for Apple silicon edge devices, working across the full ML lifecycle alongside hardware, software, and product teams. Minimum qualifications: M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience Strong foundation in deep learning theory and hands-on experience training models Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow/JAX Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion) Hands-on experience with model compression and optimization techniques (quantization, pruning, distillation, etc.) Familiarity with on-device inference frameworks such as Core ML, TensorFlow Lite, ONNX Runtime, or TensorRT Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code Preferred qualifications: Experience deploying models to embedded systems, mobile devices, or custom silicon (NPU/DSP) Familiarity with hardware-aware neural architecture search (NAS) or AutoML techniques Exposure to low-level optimization techniques such as mixed-precision training or operator fusion Hands-on experience with Apple Neural Engine and Core ML for on-device inference Publications or open-source contributions in efficient deep learning or edge AI Experience with MLOps workflows and CI/CD pipelines for model development