Staff / Principal Machine Learning Engineer - USA

4 Months ago • 6-10 Years • Artificial Intelligence

About the job

Job Description

Machine learning engineer with expertise in NLP, LLMs, PyTorch, and TensorFlow to build generative AI products.
Must have:
  • NLP experience
  • LLM training
  • Machine learning
  • PyTorch/TensorFlow
Good to have:
  • NeoX, LLaMA
  • PaLM experience
  • Generative AI
  • Production models
Perks:
  • Equity offered
  • Benefits package
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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 Ubisoft, NVIDIA, 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: Mountain View, CA, United States.

Remote location: United States.

The US base salary range for this full-time position is $240,000 - $385,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.

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$240.0K - $385.0K/yr (Outscal est.)
$312.5K/yr avg.
Mountain View, California, United States

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About The Company

Inworld powers AI-driven gameplay for video games and immersive experiences. 


Whether you’re looking to unlock novel gameplay, create content at scale, improve player immersion, or future proof your AI infrastructure, Inworld helps uplevel your game development with AI.


Inworld has worked with Xbox, Ubisoft, NVIDIA, NetEase Games, Niantic, LG UPlus, Alpine Electronics, and indie game developers to create AI-driven experiences. We’re backed by top-tier investors including Section 32, Intel Capital, Microsoft’s M12 fund, BITKRAFT Ventures, Kleiner Perkins, Founders Fund,and First Spark Ventures. 


The Inworld product suite includes: 


Inworld Engine powers real-time experiences with groundbreaking game mechanics, dynamic NPCs, and worlds that evolve with each action. AI NPCs can learn and adapt, deliver nuanced performances, perceive the world around them, and autonomously initiate actions based on players' decisions. 


Inworld Studio consists of a suite of tools that enhance game design. Using AI to streamline workflows, the Studio enables developers to workshop, draft, and outline storylines, narratives, dialogue, quests, and more.  


Inworld Core is our custom solution for future-proof AI infrastructure, including custom models, training, serving and security.

Mountain View, California, United States (Remote)

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Mountain View, California, United States (On-Site)

Vancouver, British Columbia, Canada (On-Site)

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Vancouver, British Columbia, Canada (On-Site)

Mountain View, California, United States (Remote)

Mountain View, California, United States (Remote)

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Vancouver, British Columbia, Canada (On-Site)

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