Staff Machine Learning Engineer
Why Join Inworld
Inworld is the best-funded startup in AI and games with a $500 million valuation and backing from top tier investors like Intel, Microsoft, Lightspeed, Bitkraft, Founders Fund, Kleiner Perkins, and more.
Inworld was recognized by CB Insights as one of the 100 most promising AI companies in the world and was nominated alongside Anthropic, DeepMind, OpenAI and Nvidia for Generative AI Innovator of the Year at the VentureBeat Awards 2023.
Inworld is the leading AI engine for games, enabling developers to build groundbreaking game mechanics, dynamic NPCs and worlds that evolve with each action.
Inworld powers experiences built by Niantic, NetEase Games and LG, among others, and has partnerships with key industry players such as Microsoft / Xbox, Epic Games, and Unity.
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.
Qualifications
BA / BS degree or higher in Computer Science, Engineering, or a similar technical field.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 LLMs (6B parameters and larger) such as NeoX, LLaMA, PaLM, etc.
is considered a big plus.Deep knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX.
Responsibilities
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.
The Canada base salary range for this full-time position is CAD $200,000 - $300,000. In addition to base pay, total compensation includes bonus, equity and benefits.
Within the range, individual pay is determined by work location and additional factors, including competencies and experience.
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