Data Strategy & Acquisition Lead - AI/ML Systems
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Description
We're building the next generation of AI systems - combining classic computer vision with cutting-edge multimodal LLMs. None of this is possible without the right data. As the Data Strategy & Acquisition Lead, you'll be at the heart of our AI innovation engine. You will work across teams to define, source, and deliver the high-quality, large-scale datasets that power our models. This is not just about collecting data - it's about understanding what data is needed, why it's needed, and how to get it at scale, efficiently and ethically. You'll shape how our organization understands, prioritizes, and scales data - ensuring that every dataset directly advances model capability and real-world performance. You'll partner with research scientists, hardware and software teams, operations, simulation experts, and infrastructure engineers to shape our data strategy from concept to execution.","responsibilities":"Design and evolve long-term data roadmaps aligned with research priorities and model scaling trajectories.
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Define metrics and feedback loops to evaluate dataset quality and model impact.
Define data requirements in close collaboration with Algo and Research teams for a range of CV and MM-LLM use cases.
Build acquisition strategies across modalities - including real-world collection, synthetic generation, web scraping, and partnerships.
Lead cross-functional planning and execution, coordinating with multiple cross functional engineering teams.
Own project execution, ensuring data delivery on time and at the required quality
Balance cost, scalability, and quality tradeoffs, and adapt strategies based on evolving model needs.
Track progress, unblock issues, and communicate clearly across technical and non-technical audiences.
Preferred Qualifications
Master's or PhD in a technical field (CS, EE, or related).
Experience building or managing datasets for multimodal LLMs or large-scale computer vision systems.
Familiarity with modern data infrastructure stacks - distributed storage, labeling platforms, and workflow orchestration tools.
Experience with privacy-preserving data techniques, annotation quality frameworks, and dataset bias mitigation.
Knowledge of simulation platforms, synthetic data generation, and photorealistic rendering.
Track record of building or scaling data pipelines supporting research-to-production model development.
Understanding of trends in data scaling laws, foundation models, and self-supervised learning.
Minimum Qualifications
BS and a minimum of 3 years relevant industry experience.
Experience in data strategy, data operations, or technical program management for AI/ML systems, or equivalent practical experience.
Experience managing budgets, vendors, or external data partnerships
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 .
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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