Sales - Machine Learning Engineer, Data Model
Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish!
Why Apple?
At Apple, we believe our products begin with our people. By hiring a diverse team we drive creative thought. By giving that team everything they need we drive innovation. By hiring incredible engineers we drive precision. And through our collaborative process we build memorable experiences for our customers!
We are looking for an outstanding Senior Machine Learning Engineer to develop advanced predictive modeling that drive actionable business decisions and build AI-driven personalization systems to enhance user experiences. You will apply innovative ML techniques, Generative AI, and Causal Inference Models to develop personalization and predictive models that extract meaningful insights from large-scale customer, market, and sales data.
This position provides a unique opportunity to work on real-world challenges at scale, influence critical business decisions, and innovate in the fields of predictive analytics, AI-driven personalization systems, and generative AI.
In this role, you will collaborate with a multidisciplinary team of ML engineers, data scientists, software engineers, researchers, designers and business partners to design, build, and deploy high-impact models.
You will focus on the following key areas:
- Deploy predictive models to generate actionable insights for business strategy and decision-making.
- Develop AI-driven personalization that provide tailored suggestions based on customer behavior, preferences, and historical data.
- Leverage user segmentation and clustering to enhance personalization precision for different customer groups.
- Experiment with multi-modal data (text, images, customer interactions) to improve personalization.
- Implement hybrid personalization models (Collaborative Filtering, Content-Based, Knowledge Graphs) to optimize user experiences.
- 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.
- Partner closely with software engineers to implement these models into high-performing systems and models in our production environment that can be applied to create amazing experience for our worldwide audience.
- Actively engaging in all aspects of model development, from ideation, experimentation, triaging to deployment.
- Communicate results/reports with stakeholders.
- Maintain expertise in the latest advancements in AI technology. Partnering with your team members to prepare presentations, papers, and patents for your inventions.
- 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.
- 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.