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.

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.

Conceive, design, and implement novel neural network architectures tailored for complex physical sensing, reconstruction, and inverse problems Lead end-to-end hands-on research: data preparation, model formulation, training, hyperparameter exploration, and failure-mode analysis Build, train, and rigorously validate deep learning models using Python and PyTorch Formulate custom loss functions, regularization techniques, and inductive biases that capture domain-specific constraints Design comprehensive validation and benchmarking suites to stress-test model robustness, generalization, and scaling behavior Propose creative, non-conventional modeling ideas, stay abreast of state-of-the-art DL literature, and rapidly prototype proof-of-concepts Collaborate cross-functionally with physicists and optical software engineers to benchmark models against simulated and experimental lab datasets

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

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