AIML - Sr Machine Learning Engineer, Data and ML Innovation
Job Description
Summary
Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!
As part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for 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 part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for 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.
Description
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems. The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem.
You will work with a multidisciplinary team to actively participate in Apple Intelligence’s data-model co-design and co-develop practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models.
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.
Your work may span a variety of directions, including but not limited to:
Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data.
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets.
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models.
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data.
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments.
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering.
You will work with a multidisciplinary team to actively participate in Apple Intelligence’s data-model co-design and co-develop practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models.
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.
Your work may span a variety of directions, including but not limited to:
Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data.
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets.
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models.
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data.
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments.
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering.
Minimum Qualifications
- Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning.
- Deep understanding in multi-modal foundation models.
- Staying on top of emerging trends in generative AI and multi-modal LLM.
- Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.
Preferred Qualifications
- 3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.
- Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, , etc)
- Strong problem-solving and communication skills.
- Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas
