Job Description
Job Description
LLM Research EngineerKey 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