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Apple

ML Engineer, Apple Foundation Models

Cupertino, CA$150,400 – $277,600Posted 22 days ago

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
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65% would recommend this company to other women
66% say the CEO supports gender diversity

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Join the team shaping the data foundation and intelligence for Apple's frontier foundation models. We believe that breakthrough AI capabilities are driven not only by model architecture and scale, but by the quality, diversity, and intelligence of the data used to train them. As part of the Apple Foundation Model team, you will help define how next-generation foundation models learn, reason, plan, and interact with the world, powering intelligent experiences used by billions of people. This is a rare opportunity to work at the intersection of cutting-edge AI research, large-scale training and data systems, and impactful consumer products.

Description

As a member of Apple's Foundation Models team, you will develop the data strategies, pipelines, and methodologies that drive model capability across the full training lifecycle, including pre-training, mid-training, and post-training. You will work closely with researchers, engineers, and product teams to identify capability gaps, design data-centric solutions, and create high-quality training signals for reasoning, agentic behavior, multimodal understanding, tool use, and alignment. Your work may span large-scale data curation, synthetic data generation, data recipe development, model ablation, benchmark-driven optimization, reward modeling, evaluation systems, and data flywheels that continuously improve model performance. Every dataset, evaluation, and insight you contribute will directly influence the capabilities of the foundation models powering Apple's next generation of intelligent experiences.","responsibilities":"Drive data strategy and mixture design across the foundation model training lifecycle, including pre-training, mid-training, and post-training.

Design and build scalable data generation, curation, and quality assessment systems for text, multimodal, reasoning, and agentic training data.

Develop synthetic data pipelines that enable models to learn complex capabilities such as reasoning, planning, coding, tool use, and multimodal understanding.

Create model self-improvement and self-iteration frameworks that leverage foundation models to generate, evaluate, refine, and evolve their own training data and behaviors.

Pioneer data flywheels that transform model feedback, evaluations, and user interactions into high-quality training signals for continual capability advancement.

Develop benchmark-driven methodologies to identify capability gaps, diagnose failure modes, and translate insights into targeted data interventions.

Advance frontier capabilities in reasoning, agentic systems, alignment, and long-horizon task execution.

Advance state-of-the-art techniques in data-centric AI, including reward modeling, preference learning, model self-evolution, and scalable alignment for foundation models.

Preferred Qualifications

Experience developing data-centric solutions for foundation models, especially large-scale data flywheels.

Experience improving foundation models using user interaction data, private data, or other real-world feedback signals while maintaining strong privacy and data governance standards.

Experience building agentic systems, tool-use capabilities, and reasoning models.

Experience with model self-improvement techniques.

Experience developing or improving multimodal foundation models across text, vision, audio, and video.

Minimum Qualifications

Demonstrated expertise in LLM or Multi-modal LLM with a publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying deep learning techniques to products

Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow

Ability to work in a collaborative environment

Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.

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 $150,400 and $277,600, 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.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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