Principal Applied AI Researcher - Domain- Specific Models (Dublin, CA)
Tasks
- Architect agentic research infrastructure
- Conduct supervised fine-tuning
- Define domain specific model strategy
- Deliver hands on research outputs
- Design always on evaluation harnesses
- Develop distributed training workflows
- Drive model failure analysis
- Establish evaluation methodology
- Implement post training alignment
- Influence release criteria and integration architecture
- Lead cross cutting research initiatives
- Lead domain data strategy
- Mentor researchers
- Optimize mixture experiments
- Perform continued pretraining
- Scale model training on GPU clusters
Perks/Benefits
- N/A
Skills/Tech-stack
Alignment | Benchmark design | Constitutional AI | Continued Pretraining | Data Curation | Data-parallel | Deduplication | DeepSpeed | Direct Preference Optimization | Distributed Training | Distribution Shift | Error Analysis | Evaluation methodology | Expert Grounded Assessment | Fine Tuning | Fully Sharded Data Parallel | Human Feedback | Language Models | Language Processing | Large Language Models | Learning from Human Feedback | Lifecycle Management | Machine Learning | Megatron-LM | Mixture Design | Model Lifecycle | Model Lifecycle Management | Natural Language | Natural Language Processing | Post-training | Preference optimization | PyTorch | Python | Quality scoring | Reinforcement Learning | Reinforcement Learning from Human Feedback | Retrieval | Reward Modeling | Robustness Testing | Supervised Fine Tuning
Education
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