Principal ML Scientist
Tasks
- Architect and scale agentic AI systems
- Architect and scale generative AI systems
- Build personalization and recommendation systems
- Define reusable AI architecture patterns
- Design and implement end to end AI ML platforms
- Implement model evaluation and monitoring
- Lead LLM data pipelines and model training
- Lead autonomous reasoning and decision making agent solutions
- Partner with product engineering and data teams
- Translate business problems into scalable AI solutions
Perks/Benefits
- Employee stock purchase plan
- Generous time off
- Healthcare benefits
- Life insurance
- Long-term disability
- Mental health programs
- Paid Holidays
- Parental leave
- Remote work options
- Retirement savings plan
- Short-term disability
- Tuition assistance
- Wellness days
Skills/Tech-stack
AI-assisted software development | Agent systems | Agentic AI | Cloud Platforms | Compliance | Data Pipelines | Distributed Systems | Experimentation | Generative AI | Governance | LLM RAG | Language Models | Large Language Models | MLOps | Machine Learning | Model Deployment | Model Evaluation | Model Training | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | Orchestration | Personalization | Predictive Modeling | Python | Ranking Models | Recommendation Systems | Responsible AI | Security | Software Engineering | Software development
Education
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