Applied Scientist (LLM)

📋 Description

• Design and implement prompt orchestration, fine-tuning (SFT/RLHF/DPO), and autonomous workflows
• Curate high-quality training data from large-scale text and multimodal sources
• Identify patterns in model hallucinations and visualize evaluation metrics
• Tune hyperparameters and improve inference speed/accuracy via PEFT (LoRA/QLoRA) and prompt engineering
• Collaborate with Product and Data Engineering teams to integrate LLM features into the ecosystem
• Track and report progress using industry benchmarks (MMLU, HumanEval) and internal KPIs

🎯 Requirements

• 3+ years of ML experience in NLP/LLM
• Python3, NumPy, pandas, PyTorch, and Hugging Face (Transformers, PEFT, Accelerate)
• PEFT/LoRA and RL techniques
• RAG, Fine-tuning, or Agentic frameworks
• Manage/analyze massive datasets (>100GB) across text, image, and audio
• Data pipelines and high-fidelity datasets
• Prompt engineering, agentic framework design, and LLM pipeline orchestration
• Production deployment of LLMs using Triton Inference Server, vLLM, TGI, ONNX

🎁 Benefits