Principal Applied Scientist
United States, Washington, Redmond; United States, California, Mountain View
USD 142K-274K Senior-level Full Time
Tasks
- Architect recommendation and ranking systems
- Build red teaming and adversarial safeguards
- Collaborate on safety and trust ML systems
- Define offline and online evaluation metrics
- Design data contracts and feature stores
- Develop monitoring rollout and rollback processes
- Implement counterfactual and bandit methods
- Lead content quality modeling at scale
- Mentor scientists and lead technical design reviews
- Productionize large scale machine learning models
- Run A B testing and interleaving experiments
- Scale end to end ML training and inference
- Support distributed training and inference
- Translate user behavior into model objectives
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AI Foundry | Active Learning | Adversarial Machine Learning | Azure AI | Azure AI Foundry | Azure ML | B testing | Bandit Algorithms | Calibration | Counterfactual learning | Deep learning | Distributed Training | Distributed inference | Evaluation metrics | Feature Stores | Fine Tuning | Human-in-the-loop | Interleaving | Kusto | Language Models | Large Language Models | Machine Learning | Multi-Objective Optimization | Multimodal modeling | NDCG | Objective optimization | Prompt engineering | PyTorch | Python | RAG | Ranking Systems | Recommender Systems | Red Teaming | Representation Learning | Retrieval and ranking | Retrieval-Augmented Generation | Safety and Trust ML | Sequence Modeling | Slate optimization | Synapse Analytics | TensorFlow | The Loop
Education
Regions
Countries
States
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