Job Title: Data Scientist - Large Language Model (LLM) Location: Remote Duration: Full time Job Description: As a Data Scientist specializing in large language model (LLM) applications, you will lead the charge in advancing our state-of-the-art natural language understanding and generation solutions. You will collaborate closely with our research and engineering teams to design, implement, and optimize language models, with a strong emphasis on transformers and attention networks in NLP. Your expertise will be instrumental in shaping the future of AI-driven language technologies.
Key Responsibilities: - Reinforcement Learning Expertise: Conduct advanced research and have a track record of scientific publications in reinforcement learning, including Q-learning, value-iteration methods, DQN, double DQN, actor-critic, and Proximal Policy Optimization.
- NLP and Transformers Mastery: Demonstrate deep knowledge, publications and hands-on experience with transformers and attention networks in NLP, including proficiency with the Hugging Face Transformers library and models.
- Model Development: Design, develop, and optimize large language models using cutting-edge transformer architectures and attention mechanisms, supported by proven code and projects.
- Data Structures and Algorithms: Possess a comprehensive understanding of data structures and algorithms, applying them effectively to address complex NLP challenges.
- Unix Proficiency: Be proficient in Unix-based systems to facilitate efficient data processing and model development workflows.
- Python Development: Bring at least 3-5 years of extensive Python development experience, with a focus on data science, machine learning, and AI projects.
- Prompt Engineering: Efficient and intensive prompt engineering expertise.
- LLM Infrastructure and engineering: Experience with various options for setting up the LLM infrastructure in the cloud.
Requirements: To excel in this role, you should meet the following qualifications: - Education: Hold a Master's or Ph.D. in computer science or a related field.
- Reinforcement Learning Knowledge and Publications: Present a proven track record of scientific publications in reinforcement learning, showcasing expertise in various RL methods.
- NLP and Transformers Knowledge and publications: Demonstrate in-depth understanding and hands-on experience and proven scientific publications with transformers and attention networks for NLP, including familiarity with the Hugging Face Transformers library and models.
- Fine tuning language models: Demonstrated ability to fine tune language models in multi-GPU environment.
- Data Structures and Algorithms: Possess a strong grasp of data structures and algorithms, with the ability to apply them effectively to solve intricate NLP problems.
- Unix Proficiency: Exhibit proficiency in Unix-based systems for efficient data processing and development tasks.
- Python Development: Have a minimum of 3-5 years of hands-on experience in Python development, with a particular focus on data science, machine learning, and AI. Moreover, at least 4-8 years of experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and proficiency in other Client algorithms and libraries.
- Problem Solving: Showcase exceptional problem-solving skills and a creative approach to tackling complex NLP challenges.
- Communication: Possess strong verbal and written communication skills, enabling effective collaboration with cross-functional teams.
