Senior Applied ML Scientist – Generative Video

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

$181K-$318K From the posting source checked Jun 20, 2026

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

123% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Company stage

Public-company From the posting source checked Jun 20, 2026

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior Applied ML Scientist – Generative Video Cupertino, United States of America We are looking for a passionate Senior Applied ML Researcher to drive innovation in generative video modeling, with a strong focus on diffusion-based methods and related generative features. In this role, you will research, design, and deploy models at the intersection of machine learning research and real-world product impact. You will collaborate closely with research scientists, engineers, and product teams to find novel applications of generative AI capabilities to assist our creative user base. Your mission is to elevate the workflows of millions of creators by combining generative AI with Apple's human-centered design principles. Design and train state-of-the-art generative video models, primarily based on diffusion, consistency, rectified flow, or related generative frameworks Explore novel architectures for spatiotemporal modeling (e.g., 3D U-Nets, DiT-style Transformers, hybrid CNN-Transformer models) Conduct experiments on long-range temporal coherence, motion consistency, controllability, and multi-modal conditioning (text, audio, images) Minimum Qualifications: MS in Computer Science, Machine Learning, or a related field, or equivalent practical experience 4+ years of experience in deep learning for generative models, particularly diffusion-based methods Hands-on experience in distributed training of large models using PyTorch (or equivalent frameworks) Solid understanding of video representations, spatiotemporal modeling, and neural network optimization. Experience designing & training multi-modal DiT-style Transformers, latent diffusion, or multi-stage video generation pipelines Experience design & training adapters Proven ability as a creative problem solver Preferred Qualifications: PhD with research focused on generative modeling, diffusion models, or video understanding Experience with text-to-video, image-to-video,

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