Principal Research Engineer, Model Training & Post-Training
Palo Alto, California, United States
USD 400K-550K Senior-level Full Time
Tasks
- Build evaluation and release quality systems
- Debug and improve model behavior across the stack
- Define data strategy and pipelines
- Drive model architecture and optimization
- Lead large scale distributed GPU training
- Lead training and post training strategy
- Own model improvement roadmap
- Partner to improve distributed training reliability
Perks/Benefits
- 401k matching
- Country specific visa support
- Flexible work arrangements
- Medical, dental, and vision options
- Parental leave
- Unlimited paid time off
Skills/Tech-stack
AI Feedback | Checkpointing | Cost Performance | Cost-performance tradeoffs | Data Decontamination | Data Deduplication | Deep learning | Direct Preference Optimization | Distillation | Distributed Training | Fault Tolerance | Fine Tuning | GPU clusters | Group Relative Policy Optimization | Human Feedback | Human-in-the-loop | Observability | Performance tradeoffs | Policy Optimization | Preference optimization | Regression Detection | Reinforcement Learning | Reinforcement Learning from AI Feedback | Reinforcement Learning with Human Feedback | Reproducibility | Reward Modeling | Supervised Fine Tuning | Synthetic data | The Loop | Throughput Optimization | Tool Use Fine Tuning | Tool use | Transformer
Education
Regions
Countries
States
Cities
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