Principal Applied Scientist
Core
Build AI-driven automation and agentic systems to manage infrastructure capacity, health, and automated root-cause analysis for large-scale operational data affecting billions of users.
Role type
Principal Applied Scientist (AIOps & Infrastructure)
Builds
Production ML models for anomaly detection, automated mitigation, and mathematical optimization of service efficiency and reliability.
Domain
Cloud Infrastructure / AIOps / Large-scale Data Platforms
Deliverable
production ML models
Required skills
Machine learning model development, anomaly detection, root-cause analysis, time-series forecasting, mathematical optimization, Python, PyTorch, scikit-learn, LLM-based tools, SQL, large-scale data platforms (Spark, Kusto, Hive, Azure Data Lake), cloud service operations.
Preferred skills
Experience with agentic systems, automated incident mitigation pipelines, explaining technical work to non-specialists.
Technologies
Python, PyTorch, scikit-learn, LLM APIs, SQL, Spark, Kusto, Hive, Azure Data Lake
Responsibilities
Build AI-driven automation and agentic systems for infrastructure management; develop models to detect anomalies and emerging issues; design automated root-cause analysis systems; design automated mitigation strategies; take ML prototypes to production; collaborate with engineering and product teams.
Seniority
Principal, hands-on IC with strategy & mentorship
