LLM Research Engineer

Job Description

Job Description

LLM Research Engineer

Key Responsibilities: 

• Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) for various applications. 
• Conduct research on cutting-edge techniques in natural language processing (NLP) and machine learning to improve model performance. 
• Explore advancements in transformer architectures, multi-modal models, and emergent AI behaviors. 
• Collect, clean, and preprocess large-scale text datasets from diverse sources. 
• Develop and implement data augmentation techniques to improve training data quality. 
• Ensure data is free from bias and aligned with ethical AI standards. 
• Optimize model architecture to improve accuracy, efficiency, and scalability. 
• Implement techniques to reduce latency, memory footprint, and inference time for real-time applications. 
• Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud 
• Develop robust evaluation pipelines to measure model performance using key metrics like accuracy, perplexity, BLEU, and F1 score. 
• Continuously test for bias, fairness, and robustness of language models across diverse datasets. 
• Conduct A/B testing to evaluate model improvements in real-world applications. 
  Stay updated with the latest advancements in generative AI, transformers, and NLP research. 
• Contribute to research papers, patents, and open-source projects. 
• Present findings and insights at conferences and internal knowledge-sharing sessions. 

Qualifications : 

• 7-10 years experience
• Advanced degree in CS, Artificial Intelligence, Data Science, or a related field. 
• Strong programming skills. 
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or JAX. 
• Hands-on experience with transformer-based models (e.g., GPT, BERT, RoBERTa, LLaMA). 
• Expertise in natural language processing (NLP) and sequence-to-sequence models. 
• Familiarity with Hugging Face libraries and OpenAI APIs. 
• Experience with MLOps tools like Docker, Kubernetes, and CI/CD pipelines. 
• Strong understanding of distributed computing and GPU acceleration using CUDA. 
• Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback).

Compensation: $90 - $121.86 per hour 

ID#: 36408719