Why Join Inworld

Inworld is the best-funded startup in AI and games with a $500 million valuation and backing from top tier investors including Intel Capital, Microsoft’s M12 fund, Lightspeed Venture Partners, Section 32, BITKRAFT Ventures, Kleiner Perkins, Founders Fund, and First Spark Ventures.

Inworld is the leading AI engine for games and interactive media. Inworld’s suite of AI components enables developers to build interactive, responsive, and personalized AI gaming experiences, orchestrate models to create intelligent game behaviors, and unlock enhanced productivity with AI-generated content. 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.

Inworld was recognized by CB Insights as one of the 100 most promising AI companies in the world in 2024 and was also named among LinkedIn's Top Startups of 2024 in the USA.

We are seeking Staff and Principal level Machine Learning Engineers with extensive experience in researching, designing, and implementing Machine Learning systems. You will be at the forefront of building generative AI products and agentic frameworks that utilize a wide range of modern AI stacks, including LLMs, diffusion-based synthesis models, and many more.

Qualifications

BA/BS degree or higher in Computer Science, Engineering, or a similar technical field.
6 years of experience in software development in one or more programming languages such as Python or C++.
4 years of experience in applying ML algorithms in natural language processing and/or speech processing and/or action-planning domains.
Fluency with ML frameworks such as PyTorch, TensorFlow, or JAX.
Experience in fine-tuning and evaluating LLMs, such as LLaMA, Mistral, Qwen, etc., is a significant plus.
Knowledge of working with embedded systems and/or running ML on edge devices is a big plus.

You May Be a Good Fit If You Have

Significant software engineering experience and a results-oriented mindset.
Strong problem-solving and analytical skills, with a proactive approach to tackling challenges.
Familiarity with modern AI tools and technologies, such as transformers, diffusion models, reinforcement learning.
Ability to work collaboratively in a fast-paced environment with shifting priorities.
Passion for learning and staying up-to-date with the latest advancements in machine learning research and its applications.

Responsibilities

Research and experiment with cutting-edge ML models and techniques to advance Inworld’s AI capabilities.
Develop production-scale infrastructure to train, evaluate, and serve ML models efficiently.
Provide guidance and mentorship to junior engineers, fostering a culture of learning and collaboration.

Representative Projects

Exploring the impact on quality and latency of using a new attention implementation algorithm within the LLM serving infrastructure.
Designing and implementing a high-level public API to enable developers to create new AI voices and use them at runtime.
Exploring various on-device matrix multiplication backends to optimize transformer-based speech recognition models for a specific device.

Work location: British Columbia, Canada.

The 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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Can you share your experience with deploying Large Language Models in production environments? What specific challenges did you encounter, and how did you address them?

Can you provide an example of when you led or were significantly involved in the research and experimentation of machine learning models? What were your key findings, and how did they influence the final model's design and performance in production?

What experience do you have with optimizing model training and serving on GPUs or TPUs? Can you discuss any techniques you've used to improve computational efficiency and/or reduce latency of such models?

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