Principal AI Scientist
Tasks
- Apply NLP reinforcement learning and generative AI techniques
- Architect automated evaluation pipelines
- Build LLM as judge frameworks
- Build pilot MVP prototypes and proofs of concept
- Collaborate with product engineering and enterprise partners to operationalize AI research
- Create annotation quality automation tools
- Create reusable evaluation frameworks libraries and toolkits
- Define AI training and evaluation research agenda
- Design data annotation methodologies
- Design evaluation methods for AI safety fairness and reliability
- Develop hybrid human AI quality systems
- Establish responsible AI evaluation bias mitigation explainability and governance standards
- Implement human in the loop learning
- Lead AI model training and evaluation
- Mentor data scientists and research engineers
- Own end to end technical development lifecycle
- Provide technical guidance on model pipelines and ML infrastructure
- Publish research papers and white papers
- Represent company at industry conferences
- Run model evaluations benchmarking and fine tuning
- Translate research into scalable solutions
Perks/Benefits
- 401k match
- Birthday day off
- Company holidays
- Flexible work arrangements
- Paid parental leave
- Paid time off
- Professional development
- Volunteer time off
Skills/Tech-stack
AI Governance | AI alignment | Agentic Systems | Benchmarking | Bias Mitigation | Data Annotation | Deep learning | Explainability | Fine Tuning | Generative AI | Human-in-the-loop | LLM-as-a-Judge | Language Models | Large Language Models | Model Evaluation | Model Optimization | NLP | Prompt engineering | PyTorch | Python | Reinforcement Learning | Reproducible Research | TensorFlow
Education
Roles
AI | AI Scientist | Principal | Principal AI Scientist | Scientist
Regions
Countries
States
Cities
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