AIML - Machine Learning Engineer, Data & Machine Learning Innovation

As a Machine Learning Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in foundation models to tackle complex problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of data processing pipeline, modeling methodology and effective evaluation metrics. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. Minimum Qualifications Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning. Deep understanding of multi-modal foundation models. Strong software development skills with proficiency in Python; hands-on experience working with deep learning toolkits like PyTorch, TensorFlow, or JAX. BS/MS in STEM with 7+ years of experience developing and evaluating ML applications, demonstrating a passion for understanding and improving model/data quality. Preferred Qualifications - Deep understanding of multi-modal foundation models. - Staying up-to-date with emerging trends in generative AI and multi-modal LLMs. - The ability to formulate machine learning problems, design, experiment, implement, and communicate solutions effectively. - Hands-on mentality to own engineering projects from inception to shipping products and the ability to work independently and as part of a cross-functional team. - Demonstrated publication records in relevant conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc.). - Track records of adopting ML to solve cross-disciplinary problems. - Demonstrated leadership in advancing machine learning research and development, including driving innovative projects, mentoring team members, or leading collaborations that resulted in impactful outcomes.