> Markdown version of [/jobs/ext/3683449-machine-learning-engineer-multimodal-intelligence](https://www.wearedevelopers.com/jobs/ext/3683449-machine-learning-engineer-multimodal-intelligence). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - Multimodal Intelligence - **Company:** Apple Inc. - **Location:** Sunnyvale, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Computer Vision, Big Data, Distributed Systems, Hardware Design, Python (Programming Language), Machine Learning, Rapid Prototyping Process, Regression Testing, Pytorch, Deep Learning, Information Technology, Low Latency, Machine Learning Operations - **Published:** October 11, 2026 - **Apply:** https://www.juju.com/job/1_apple2-200683687 ## About the Role You will work closely with modeling, platform, hardware, and product engineering teams across Apple - taking future hardware design and product needs into account as you make implementation decisions - and you will have the opportunity to collaborate broadly to deliver the best possible products. **Minimum Qualifications** + Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g., vision, language, video, audio, etc.) + Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX + Experience with rapid prototyping, reproduction, and validation of research ideas + Ability to work in a collaborative environment + Ability to communicate the results of analyses in a clear and effective manner + BS and a minimum of 3 years of relevant industry experience **Preferred Qualifications** + Master's or PhD, or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field + Deep expertise in multimodal foundation models, with a focus on practical applications + Track record of translating research into practical applications either through published work or industry experience + Strong applied research experience in at least one major area of model development (data curation, pre-training, fine-tuning, alignment, or evaluation), particularly as it applies to multimodal systems + Experience with large-scale training pipelines, including working with large datasets and scaling models across distributed systems + Experience bridging research ideas with production constraints Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. ## Description The Multimodal Intelligence team builds and ships the Computer Vision and Machine Learning systems behind Apple Intelligence - spanning data collection and curation, training and fine-tuning, evaluation, optimization, and on-device deployment. Our team has an established track record of delivering features that combine Apple's sensing hardware with large foundation models, including Visual Intelligence and the on-device foundation models that power text and visual understanding across iPhone, iPad, Mac, and Apple Vision Pro. We are focused on building experiences where a device can see, read, and reason about the world around it - privately, responsively, and on-device wherever possible. **Description** We are looking for a Machine Learning Engineer to build the pipelines, infrastructure, and production systems that turn multimodal foundation models into shipping Apple Intelligence features. You will own end-to-end model delivery: building and scaling data curation and training pipelines, fine-tuning and optimizing large multimodal models for on-device and hybrid execution, standing up reproducible evaluation and regression testing for text and visual understanding, and hardening promising approaches into robust, maintainable production systems under real latency, memory, power, and privacy constraints.