Q - Machine Learning Researcher – Edge & Efficient AI
Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on May 28, 2026. Apple scores A on the Alion truth index.
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.
Description
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