Machine Learning Engineer – Edge & Efficient AI

Summary 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.

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.

Responsibilities

• Research and design state-of-the-art deep learning models optimized for resource-constrained Apple silicon edge devices
• Drive projects across the full ML lifecycle, from ideation and experimentation to production deployment
• Collaborate closely with cross-functional teams including hardware, software, and product
• Continuously evaluate and adopt new techniques to improve model performance and efficiency on-device

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

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Role Number: 200665634-0865