Research scientist

3 weeks ago

Bengaluru, India Gnani.ai Full time
Overview:
We are seeking a skilled LLM (Large Language Model) Research Scientist to join our innovative team. The ideal candidate will have extensive experience in pretraining and continual pretraining of LLMs, including familiarity with models like Llama 3.1, Mistral, and experience with Indic languages. This role is essential for advancing our capabilities in natural language processing (NLP) and generative AI.
Key Responsibilities
Model Development and Research: Design, implement, and evaluate state-of-the-art LLM architectures, focusing on pretraining methodologies that enhance model performance. Stay updated on the latest advancements in LLM research, contributing new ideas and techniques to optimize model training processes.
Pretraining and Fine-tuning: Conduct rigorous pretraining of LLMs on large datasets, ensuring robust data preparation, model configuration, and performance evaluation. Develop effective fine-tuning strategies to adapt models to specific tasks and domains.
Continual Learning: Explore techniques for continual learning, enabling models to adapt to evolving datasets and tasks, improving their relevance and accuracy over time.
Ethical AI: Incorporate ethical guidelines into LLM development, ensuring fairness, bias mitigation, and responsible AI practices. Develop methods to assess and mitigate potential biases in models.
Prompt Engineering: Research and develop effective prompt engineering techniques to optimize model performance and user experience.
Model Evaluation and Benchmarking: Establish rigorous evaluation metrics and benchmarks to assess model quality and progress. Develop custom evaluation datasets to address specific use cases.
Model Deployment and Infrastructure: Collaborate with infrastructure teams to deploy and scale LLMs efficiently. Optimize models for deployment on edge devices or cloud platforms.
Collaboration: Work closely with cross-functional teams, including product, engineering, and design teams, to integrate LLMs into applications and ensure alignment with business objectives.
NLU Task Development: Research and develop models for various NLU tasks, including:
Text Summarization
Machine Translation
Text Classification
Sentiment Analysis
Question Answering
Text Generation
Dialogue Systems
Information Extraction
Textual Entailment
Qualifications
Education: Ph. D. in Computer Science, Machine Learning, or a related field.
Technical Skills:
Strong background in machine learning, deep learning, and natural language processing.
Proven experience with pretraining techniques for LLMs, including data collection, processing, and augmentation strategies.
Familiarity with state-of-the-art LLM architectures like Llama 3, Mistral, and other advanced models.
Experience working with multilingual datasets, particularly those involving Indic languages.
Proficiency in Python and experience with deep learning frameworks (e.g., Tensor Flow, Py Torch).
Expertise in large-scale distributed training and optimization techniques.
Knowledge of reinforcement learning and its application to LLM fine-tuning.
Strong understanding of natural language understanding (NLU) tasks.
Experience with generative AI techniques, including text generation, code generation, and image generation.
Proficiency in data science and statistical analysis.
Research Experience: Demonstrated ability to conduct independent research, publish papers at top-tier conferences, and contribute to open-source projects.

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