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Apple Machine Learning Engineer - Ads Relevance & Quality United States, California, Cupertino 203235576
Today
You’ll work at the intersection of applied ML, NLP, and content quality—designing models and systems that understand queries, flag inappropriate content, and raise the bar for ad relevance and user trust across billions of queries and impressions.
- 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
- 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
- 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
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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