Python AI/ML Software Engineer

at  Waverley Software

JOB SUMMARY:

We are seeking an experienced AI/ML Expert to join our cutting-edge research team that is dedicated to developing an innovative AI-based Game Design Assistant. This role involves exploring, designing, and implementing state-of-the-art LLMs and ML algorithms to facilitate and enhance game design processes, enabling creators to efficiently generate engaging content, dynamic narratives, and immersive game worlds.

REQUIRED SKILLS & QUALIFICATIONS:

• Proven experience in developing and implementing ML models
• Proficiency in Python, and experience with ML frameworks (TensorFlow, PyTorch, etc.). Proven expertise in LangChain, OpenAI API, or similar.
• A solid understanding of LLMs, Generative AI, Natural Language Processing, and their applications in content generation and creative assistance.
• Creative problem-solving skills and the ability to work on complex, interdisciplinary projects.
• Excellent communication skills, with the ability to collaborate effectively with both technical and non-technical team members.
• Passion for AI and a keen understanding of design principles and challenges.

PREFERRED QUALIFICATIONS:

• Experience with game development engines and tools (Unity, Unreal Engine, etc.).
• Experience with cloud computing platforms and services for ML model deployment.

• Conduct in-depth research on the latest advancements in LLMs, ML algorithms, and their applications in game design and development.
• Collaborate with the research team to ideate, prototype, and refine AI-driven solutions that address complex game design challenges.
• Develop and implement ML models that can assist in procedural content generation, dynamic difficulty adjustment, player behavior analysis, and other aspects of game design.
• Work closely with game designers to understand and translate their needs into AI-based tools and functionalities that enhance creativity and productivity.
• Continuously evaluate the performance of implemented models, making adjustments and optimizations to improve efficiency and effectiveness.
• Document research findings, model architectures, and algorithms, contributing to the team’s knowledge base and facilitating knowledge transfer.