Posted Aug 3

Inworld is hiring a
Staff/Principal Machine Learning Engineer - Canada

Why Join Inworld

Inworld is a developer platform for building AI characters. We go beyond large language models (LLMs) and add performance, configurable safety, knowledge, memory, narrative controls, multimodality, and more. We’re focused on enabling character-based interactions for immersive experiences like video games, brand activations, and training simulations. 

Inworld uses advanced AI to build generative characters whose personalities, thoughts, memories, and behaviors are designed to mimic the deeply social nature of human interaction. Our platform lets you create characters with personality and contextual awareness to keep them in-world and on brand. Integrations make it easy for developers to deploy characters into immersive experiences, while scale and performance are optimized for real-time experiences. 

Inworld AI is funded by top-tier investors, including Kleiner Perkins, Intel, Microsoft, and Founders Fund, and a team of all-star angels - corporate executives, top VC funds' partners, and industry veterans from Riot Games, Twitch, and Oculus.

We are seeking Staff and Principal level Machine Learning Engineers with extensive experience in Natural Language Processing (NLP). You will be at the forefront of building generative AI products that utilize Large Language Models (LLMs) to create next-generation AI characters.

• Bachelor’s degree or equivalent practical experience.
• 6 years of experience with software development in one or more programming languages. 
• 4 years of experience with applying machine learning algorithms in natural language processing domains.
• 1+ years of experience training or fine-tuning generative LLMs (6B parameters and larger) such as GPT3, PaLM, etc. is considered a plus.
• Deep knowledge of machine learning frameworks such as PyTorch or JAX. 

• Explore and experiment with cutting edge ML techniques for applied NLP.
• Develop and test production-grade scalable generative machine learning models.

In-office location: Vancouver, Canada.

Remote location: Canada.
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