As part of Apple's AI and Machine Learning org, we inspire and create groundbreaking technology for large language models, multi-modal models, with strong agent and reasoning capabilities. The Data and Machine Learning Innovation (DMLI) team is looking for a passionate Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges. As a team member, you will work on some of the most ambitious technical challenges in the field. Your role will involve collaborating closely with our team of machine learning researchers, engineers, and data scientists. Together, you will spearhead groundbreaking research initiatives and develop transformative products designed to create a significant impact for billions of users worldwide.
Description
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.
