Applied Machine Learning Researcher
Sunnyvale, California, United States
Apple
We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways.Summary
Posted: Dec 21, 2024Role Number:200584357
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Multifaceted, amazing people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices, strengthening our commitment to leave the world better than we found it. Join us in this truly exciting era of Artificial Intelligence to help deliver the next groundbreaking Apple products & experiences! We are continuously advancing the state of the art in Computer Vision and Machine Learning, touching all aspects of language and multimodal foundation models, from data collection, data curation to modeling, evaluation and deployment. As a member of our dynamic group, you will have the unique and rewarding opportunity to craft upcoming research directions in the field of multimodal foundation models that will inspire future Apple products. You will be working alongside highly accomplished and deeply technical scientists and engineers to develop state of the art solutions for challenging problems. This is a unique opportunity to be part of what forms the future of Apple products that will touch the lives of many people. We (Multimodal Intelligence Team) are looking for a machine learning researcher to work on the field of Generative AI and multimodal foundation models. Our team has an established track record of shipping features that leverage multiple sensors, such as FaceID, RoomPlan and hand tracking in VisionPro, as well as a strong research presence in the multimodal AI community. Our publications span multimodal pre-training, vision-language models, video-language models, and multimodal alignment. We are focused on building experiences that demonstrate the power of our sensing hardware as well as large foundation models.
Description
This position requires a highly motivated person who wants to help us bridge the gap between research advances and practical applications in generative AI and multimodal foundation models. You will be responsible for evaluating and adapting emerging research, conducting applied research experiments, and working with engineering teams to transform promising approaches into robust solutions, taking into account future hardware design and product needs. In addition, you will have an opportunity to engage and collaborate with several teams across Apple to deliver the best products.
Minimum Qualifications
- Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g. vision, language, video, 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 relevant industry experience.
Preferred Qualifications
- Master's or PhD, or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field.
- Track record of translating research into practical applications either through published work or industry experience.
- Deep expertise in multimodal foundation models, with a focus on practical applications.
- 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.
Pay & Benefits
- At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $143,100 and $264,200, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure 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. Learn more about your EEO rights as an applicant.
Tags: Computer Science Computer Vision Deep Learning Distributed Systems Engineering Generative AI JAX Machine Learning ML models PhD Pipelines Prototyping Python PyTorch Research
Perks/benefits: Career development Equity / stock options Health care Medical leave Relocation support
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