Principal Applied Research Scientist – Generative AI and NLP
Tasks
- Apply quantization aware training for scalable inference
- Build and standardize evaluation frameworks for generative systems
- Create synthetic evaluation datasets
- Develop training and evaluation workflows for high throughput production
- Fine tune and adapt open source foundation models using parameter efficient methods
- Implement knowledge distillation for latency constrained models
- Integrate AI assisted coding tools into research prototyping
- Lead research strategy for generative AI and deep learning models for email data
- Mentor researchers and establish model validation best practices
- Set technical direction for agent based systems and tool use paradigms
- Validate model outputs with human in the loop review
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | Agent Orchestration | DPO | Data Pipelines | Deep learning | Distributed Training | Efficient Fine Tuning | Encoder Decoder Transformer | Encoder-decoder | Evaluation Frameworks | Fine Tuning | Hugging Face | Human-in-the-loop | Inference Optimization | Knowledge Distillation | LLM-as-a-Judge | Language Models | Language Processing | Large Language Models | LoRA | Machine Learning | Model Compression | Multi-Agent | Multi-agent orchestration | Multi-step reasoning | Natural Language | Natural Language Processing | Parameter efficient fine-tuning | Prompt engineering | Pseudo-labeling | PyTorch | Python | Quantization aware training | RLHF | Synthetic data | TensorFlow | The Loop | Tool use | Transformer Architecture | Transformer fine-tuning
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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