Machine Learning Engineer - Product Marketing Customer Analytics
Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Jan 23, 2025. Apple scores A on the Alion truth index.
At Apple, new ideas have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! The Product Marketing Customer Analytics team is seeking a Machine Learning Engineer with deep technical experience in predictive analytics and analytic engineering.
Description
Support Product Marketing, Investor Relations, and the Executive Team with predictive analytics for customer product and services engagement. Understand product requirements then translate them into modeling tasks and engineering tasks
Develop scalable ML algorithms and models to understand customer behavior and provide leadership with actionable insights and recommendations
Design and implement end-to-end machine learning pipelines-from feature engineering to model serving- using best in class MLOps frameworks
Develop and optimize deep learning and traditional ML solutions on high-volume datasets using GPU clusters or distributed CPU environments.
Experiment with cutting-edge algorithms, providing advanced insights into customer behavior and engagement.
Manage ML projects through all phases, including data quality, algorithm/feature development, predictive modeling, visualization, and deployment and maintenance.
Tackle difficult, non-routine analysis/prediction problems, applying advanced ML methods as needed.
Partner with peers to build and prototype analysis pipelines that provide insights at scale.
Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models. Enhance and evolve solutions to meet changing business needs with agility.
Minimum Qualifications
8+ years of hands-on programming skills for large-scale data processing
Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field
Preferred Qualifications
Excellent understanding of analytical methods and machine learning algorithms including regression, clustering, classification, optimization, and other advanced analytic techniques.
8+ years of proven experience building and scaling predictive models across distributed systems (eg: Spark, Kubernetes, GPU clusters), production model hosting, and handling end-to-end performance optimization to solve business problems.
8+ years of hands-on programming skills (Python, and/or Spark) for large-scale data processing, deriving key insights, developing machine learning models on structured and unstructured data, and with demonstrated success maintaining robust, high-throughput ML pipelines in a production environment.
Comfortable with advanced deep learning frameworks (Tensorflow, PyTorch) and adept at designing and scaling ML platforms that include feature stores, automated retraining pipelines and CI/CD integration. Able to design systems to handle high-volume ML workflows and implement scalable, fault-tolerant solutions.
Solid technical database and data modeling knowledge (Oracle, Hadoop, SnowFlake), and experience optimizing SQL queries on large dataset for performance-critical analytics.
Able to work effectively on ambiguous data and constructs within a fast-changing environment, tight deadlines and priority changes
Strong communication skills and ability to explain complex technical topics to both data science peers and non-technical business stakeholders, effectively presenting findings and recommendations to senior executives.
Demonstrated success in partnering cross-functionally, guiding diverse technical teams, aligning business stakeholders, invested in collective success of teams and project outcomes.