Description

Description

You'll play a key role in shaping the future of safe, high-quality advertising at Apple. Your work will help ensure that ads remain useful, relevant, and respectful of our users-supporting Apple's values of privacy, trust, and transparency. You'll collaborate with world-class engineers and researchers, apply cutting-edge ML techniques in real-world systems, and have a direct impact on the experience of millions of users every day.

Responsibilities

• Design and implement machine learning models to evaluate and improve ad relevance, trust, and quality for user queries
• Build NLP and multi-modal models that detect offensive, unsafe, or policy-violating content at scale
• Develop methods for semantic query understanding, ads understanding, relevance scoring, and keyword-to-ad matching
• Collaborate closely with product and policy teams to translate content integrity standards into measurable ML objectives
• Work with large-scale, privacy-preserving datasets to discover and operationalize new quality signals
• Conduct offline/online experiments to measure impact on user trust and satisfaction across Ads
• Partner cross-functionally with infrastructure, product, and moderation teams to deploy models at production scale

Minimum Qualifications

• 4+ years of experience applying machine learning at scale in domains such as ad tech, content moderation, search ranking, or recommendation systems
• Strong expertise in natural language processing, including offensive content detection, semantic matching
• Experience with Transformer-based architectures (e.g., BERT, DistilBERT) and training pipelines in TensorFlow or PyTorch
• Familiarity with fine-tuning Large Language Models (LLMs) for downstream tasks such as classification, content moderation, or semantic relevance
• Familiarity with quality and fairness evaluation frameworks (precision, recall, coverage, policy alignment, etc.)
• Hands-on experience with A/B testing, experimentation frameworks, and performance debugging in production
• Proficiency in Python and SQL
• Strong problem-solving and communication skills with a focus on translating abstract trust/safety goals into deployable solutions
• MS in Computer Science, Machine Learning, NLP, or a related technical field

Preferred Qualifications

• 7+ years of experience applying machine learning at scale in domains such as ad tech, content moderation, search ranking, or recommendation systems
• PhD in Computer Science, Machine Learning, NLP, or a related technical field
• Additional experience in Scala or Java

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 .