Applied Machine Learning Research Engineer - Multimodal LLMs for Human Understanding

We're starting to see the incredible potential of multimodal foundation and large language models, and many applications in the computer vision and machine learning domain that previously appeared infeasible are now within reach. We are looking for a highly motivated and skilled Applied Machine Learning Research Engineer to join our team in the Video Computer Vision group and help us push the boundaries of human understanding. The Video Computer Vision org has pioneered human-centric real-time features such as FaceID, FaceKit, and Gaze and Hand gesture control which have changed the way millions of users interact with their devices. We balance research and product requirements to deliver Apple quality, pioneering experiences, innovating through the full stack, and partnering with HW, SW and AI teams to shape Apple's products and bring our vision to life.

Description

You'll work on ground breaking research projects to advance our AI and computer vision capabilities, contribute to both foundational research and practical applications on multimodal large language models, and design, implement, and evaluate algorithms and models for human understanding. You have a strong background in developing and exploring multimodal large language models that integrate diverse data modalities such as text, image, video, and audio. You'll have the opportunity to collaborate with multi-functional teams, including researchers, data scientists, software engineers, human interface designers and application domain experts. You'll stay up-to-date on the latest advancements in AI, machine learning, and computer vision and apply this knowledge to drive innovation within the company.

Minimum Qualifications

• Experience in developing, training/tuning multimodal LLMs.
• Programming skills in Python.
• Bachelors degree and a minimum of 3 years relevant industry experience.

Preferred Qualifications

• Expertise in one or more of: computer vision, NLP, multimodal fusion, Generative AI.
• Experience with at least one deep learning framework such as JAX, PyTorch, or similar.
• Publication record in relevant venues.
• PhD in Computer Science, Electrical Engineering, or a related field with a focus on AI, machine learning, or computer vision.

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 $147,400 and $272,100, 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 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. Learn more about your EEO rights as an applicant .

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Client-provided location(s): Sunnyvale, CA

Job ID: apple-200613298

Employment Type: OTHER

Posted: 2025-07-24T12:03:49