As a Machine Learning Engineer on the Maps Data Engineering team youll design and deploy advanced models that fuse satellite imagery aerial photos behavioral signals rich map metadata and hundreds of internal data sources to power next-generation user experiences. Youll also pioneer the development of generative AIdriven multi-agent systems capable of dynamic reasoning evaluation and self-improvementscaling the accuracy precision and quality of map data that millions of users rely on every role offers the opportunity to solve some of the most complex real-world challenges at the intersection of geospatial intelligence computer vision large language models and generative Apple we create products that enrich peoples lives; and we believe even the smallest moments like arriving confidently at the right destination are part of that promise. As a member of the Maps Data Engineering team youll help transform intricate geospatial systems into seamless intuitive experiences; turning advanced technology into everyday magic for hundreds of millions of users worldwide.
5 years of experience in machine learning engineering or applied data science with a consistent record of delivering production-grade ML systems.
8 years of software product engineering experience.
Strong background in machine learning computer vision NLP or generative AI with hands-on expertise applying these techniques to large-scale data.
Deep familiarity with LLMs transformers and the HuggingFace ecosystem; ability to fine-tune optimize and deploy models in production.
Proven grounding in statistical modeling design and predictive analytics to drive decisions.
Expert-level proficiency in Python and command of data science libraries (e.g. NumPy Pandas Polars Scikit-learn) and ML frameworks (PyTorch TensorFlow).
Proficiency in data visualization for analysis model diagnostics and communicating sophisticated findings (e.g. Matplotlib Seaborn Plotly).
Excellent communication leadership and mentoring skills with the ability to guide junior engineers and collaborate effectively across diverse teams.
A track record of publications in credible machine learning conferences (e.g. NeurIPS ICML ACL) or relevant journals
Contributions to publicly available models or a strong performance record on Kaggle or other machine learning competitions.
Past experience working directly with geospatial data mapping technologies or location-based services.
A strong conceptual understanding of distributed data and compute systems event streaming platforms (e.g. Kafka) and modern data storage formats.
Advanced degree (MS/PhD or equivalent experience) in Computer Science Machine Learning AI or related field or equivalent practical experience.
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