Senior Machine Learning Engineer - Maps Data

Senior Machine Learning Engineer - Maps Data

Geospatial data engineering defines real-world user experiences through Maps. As we make the Maps experience more intelligent and context-aware, it becomes central to delivering precision and confidence; helping users seamlessly search, find, and reach the exact place or address they intend to go. Whether it's powering navigation, enabling detailed address search, enhancing map display, or driving rich interactions with places, our work builds the intelligence behind these crucial experiences. This foundation supports hundreds of millions of Apple Maps users every day, crafting how they explore and connect with the world around them.

Responsibilities

• As a Senior Machine Learning Engineer, you will be a technical leader responsible for the entire lifecycle of our data intelligence systems.
• Architect and own production ML systems: Lead the full lifecycle of ML solutions from research and prototyping through deployment, monitoring, and large-scale optimization.
• Ensure reliability, efficiency, and measurable impact of production ML systems.
• Drive data intelligence with advanced models: Develop and fine tune sophisticated models, including LLMs/transformers and computer vision models, to extract insights, detect anomalies, and improve geospatial data quality.
• Design and operationalize generative AI driven multi-agent systems capable of reasoning, evaluating, and self-improving at scale.
• Lead multi-functional innovation: Collaborate with data engineers, product managers, and operations teams to translate complex business needs into scalable technical solutions.
• Integrate ML systems seamlessly into the broader Maps ecosystem to enhance navigation, search, and place experiences.
• Elevate engineering perfection: Champion standard processes in ML engineering and MLOps, including reproducibility, monitoring, and CI/CD for ML.
• Mentor and guide other engineers, fostering a culture of technical rigor, innovation, and continuous improvement.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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.