Deep Learning Research Engineer - ML & Physics - Q

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

Apple designs consumer electronics that have touched millions and changed the way people interact with electronic devices around the world. The ML-Physics team works at the intersection of machine learning and physics, building models that leverage both data-driven learning and deep physical understanding to solve complex sensing and optical measurement challenges.

We are seeking a creative and hands-on Deep Learning Research Engineer to drive model and architecture innovation across our sensing platforms. In this role, you will go beyond off-the-shelf models, exploring novel neural network designs, designing rigorous training and validation pipelines, and translating inventive concepts into clean, high-performance implementations. While our domain deals with physical data, this is an ML-first role: we are looking for a deep learning specialist with strong research intuition, expertise in PyTorch, and the drive to solve non-standard, real-world problems.

Minimum Qualifications

M.Sc. in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, Physics, or a related technical field.

3+ years of hands-on research experience in machine learning and deep learning (in industry R&D or an academic research lab)

Deep theoretical and practical understanding of modern deep learning fundamentals (e.g., neural network architectures, optimization landscapes, generalization, attention/transformers, CNNs, or implicit representations)

Proven hands-on proficiency in Python and PyTorch, including writing custom layers, loss functions, and efficient training loops

Demonstrated track record of creative problem-solving, proposing novel ideas, and executing them from hypothesis to experimental validation

Strong technical communication skills and fluency in English

Preferred Qualifications

Track record of research contributions (e.g., publications in top-tier conferences like CVPR, ICCV, NeurIPS, ICLR, ICML, or relevant patents)

Foundational understanding or interest in physics, wave optics, signal processing, or computational imaging

Experience with physics-informed neural networks (PINNs), neural operators (FNO/DeepONet), or integrating physical differential equations into loss functions.

Experience with large-scale distributed training, model profiling, and GPU acceleration