Machine Learning Research Scientist - Health AIML

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 last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

154% above the BLS national median 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.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Research Scientist - Health AIML Seattle, United States of America The Health AIML team is at the forefront of machine learning and health science at Apple. We are a close-knit team of research scientists, software engineers and machine learning engineers passionate about delivering innovative technologies that impact millions of users. We are looking for a Machine Learning Research Scientist with strong dedication to solving real-world problems in health and fitness that enrich our customers' lives. We're developing next-generation multimodal models to create intelligent health and fitness experiences. This role requires someone with strong expertise in large multimodal models to work at the intersection of AI and health to build foundational models that scale to billions of users worldwide. Your work will shape the future of health and fitness technologies at Apple. We are looking for a research lead to guide multimodality research. You will lead the development of foundational technology that enables models to understand health and fitness data. Lead research into health and fitness representation models and multimodal models. Design, prototype and scale up new architectures to improve model intelligence. Execute and analyze experiments autonomously and collaboratively. Study, debug, and optimize model performance and computational performance. Contribute to training and inference infrastructure. Guide technical and architectural decision. Minimum Qualifications: PhD in Computer Science/Engineering, Machine Learning, Statistics, Mathematics or related field. Industry work experience. Experience landing contributions to major LLM training runs. Proven track record of publishing SOTA. Strong skills with deep learning frameworks such as PyTorch, JAX,

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