Remote Lead AI Engineer

Summary

Join our team as a Lead AI Engineer and play a pivotal role in identifying, benchmarking, and implementing superior AI models to elevate our conversational AI platform. You will lead projects aimed at enhancing the scalability, accuracy, and realism of our AI-driven characters, and improve the automation and emotional intelligence of our AI responses.

Requirements

• Bachelor’s degree in Computer Science, AI, Machine Learning, or a related field
• Minimum of 1 year of hands-on experience with Large Language Models (LLMs) and AI conversational platforms
• Proven track record in AI model benchmarking, NLP, and machine learning algorithms
• Experience with character AI and animation integration is highly desirable
• Strong programming skills in Python, including experience with AI and machine learning libraries
• Ability to work independently in a remote setting, with strong communication skills and a collaborative team spirit

Responsibilities

• Conversational quality optimisations
• Identify, evaluate and validate superior AI models to enhance conversational experiences for different use case like romantic relationships, coaching, education and roleplay
• Implement and maintain a test environment for consistent performance evaluation
• Collaborate with team members to define benchmarking questions and approaches, ensuring the model's integration is seamless and efficient
• Test the product yourself regularly and review user feedback together with team to identify ways to improve the conversation quality
• Refine and optimise context prompt data structures to enable user customisation
• Create controls and validation to ensure the quality of AI conversation across characters, both internal created and user generated
• Enable character scaling
• Validate and repurpose character data sets for enhancing LLM prompting techniques
• Develop automation pipelines for dataflows to facilitate easier integration and scalability
• Enhance AI response automation by improving emotion classification accuracy and the decision-making process for memory retention
• Address challenges related to automated responses and animation diversity by optimizing the call structure to the LLM
• Innovate solutions to ensure more human-like, dynamic and varied emotional expressions, contributing to a more realistic and engaging user experience
• Lead efforts to retrain Large Language Models (LLMs) using unique datasets to significantly improve the human-like quality of the conversational experience for a range of use cases