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Apple Sales - Machine Learning Engineer Data Model Singapore 843466266
25.03.2025
Description
You will focus on the following key areas:- Build real-time personalization pipelines that can dynamically adjust based on live user interactions.- Lead the exploration for predictive modeling of Large Language Models and Generative AI, Causal Inference Model, GNN, venturing into new areas within these fields.- Turn prototypes into automated pipelines and deploying them to production; deciding when to use out-of-the-box solutions vs. building custom solutions or a hybrid approach.- Analyze and preprocess large scale datasets to extract meaningful patterns and ensure model accuracy.- Ongoing data analysis to build new or fine-tune existing models to optimize results.- Actively engaging in all aspects of model development, from ideation, experimentation, triaging to deployment.- Communicate results/reports with stakeholders.
Minimum Qualifications
- 5+ years of professional experience in building and deploying predictive models and AI-driven personalization at scale.
- Ph.D. in Computer Science, Artificial Intelligence, Machine Learning or related field; or
- M.S. in related field with 3+ years experience applying machine learning engineer to real business problems.
Preferred Qualifications
- Proven expertise in data preprocessing, feature engineering, and analyzing large datasets to extract meaningful patterns.
- Strong knowledge of state-of-the-art ML algorithms, including Generative AI, Multi-modal LLMs.
- Solid understanding of insight modeling (Causal Inference Model, GNN, Generative AI, Forecasting).
- Hands-on experience in forecasting models, anomaly detection, and AI-driven personalization (matrix factorization, contextual recommendation, collaborative filtering).
- Proficiency in Python and key ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn).
- Experience working with Big Data tools (SQL, Spark, Hadoop) and cloud-based ML pipelines.
- Track record of deploying ML models into production and optimizing for performance and scalability.
- Excellent communication and soft skills.
- Strong portfolio of shipped ML products, patents, or published research is a plus.
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