Machine Learning Engineer Platform - iCloud Mail Intelligence
Tasks
- Build machine learning infrastructure
- Build real time and batch inference
- Conduct LLM evaluation
- Create embedding and retrieval systems
- Deploy ML models to production
- Design distributed data processing systems
- Fine-tune LLMs
- Implement end-to-end ML pipelines
- Implement event-driven architectures
- Implement prompt engineering
- Improve Mail Calendar and Contacts experiences
- Leverage privacy preserving ML techniques
- Perform feature engineering
- Serve and monitor ML models
- Train evaluate and operate ML models
Perks/Benefits
- N/A
Skills/Tech-stack
Artificial Intelligence | Batch inference | Big Data | Data Processing | Data handling | Device Machine Learning | Distributed Systems | Embeddings | End to End | End-to-End ML Pipelines | End-to-End Testing | End-to-end ML | Event Driven | Event-driven architecture | Feature Engineering | Fine Tuning | Go | Java | Kotlin | LLM Evaluation | ML pipelines | Machine Learning | Machine Learning Model | Machine Learning Model Serving | Model Monitoring | Model Serving | On-device Machine Learning | Predictive Modeling | Privacy-preserving | Privacy-preserving machine learning | Prompt engineering | Python | Real Time | Real-time Inference | Responsible Data Handling | Retrieval systems | Scala | Supervised Learning | Unit Testing | Unsupervised Learning
Education
Regions
Countries
States
Cities
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