AssemblyAI's logo. A blue illustration on the left, and the word Assembly A I on the right. The illustration is the letter V inverted. Why? I don't know either. Looks cool though.
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icon image representing an audio disk
icon image representing a stylized lemur
icon image representing sound waves
Core TranscriptionConvert audio files, video files, and live speech into text.
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Audio IntelligenceInterpret your audio for business and personal workflows.
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LeMURThe easiest way to build LLM apps on voice data.
Introducing LeMUR
The easiest way to build LLM apps on voice data.
Introducing Conformer-2
Our latest AI model for automatic speech recognition trained on 1.1M hours of audio data that achieves state-of-the-art results for English speech to text conversion.
How ChatGPT actually works
Explore the mechanics behind ChatGPT.
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About us

We are AssemblyAI

At AssemblyAI, we build on the latest AI breakthroughs to offer state-of-the-art, production-ready AI models.

Our mission

Our mission is to make State-of-the-art AI models accessible to developers and product teams at an extremely large scale through a simple API.

We've raised over $63M in funding from leading investors, including Insight Partners, Accel, and Y Combinator.
AssemblyAI today announced that it raised $30 million in a Series B round led by Insight Partners with participation from Y Combinator and Accel. To date, AssemblyAI has raised $64 million, which founder and CEO Dylan Fox tells TechCrunch is being invested in growing the company's research and engineering teams and data center capacity AI model training.

Flush with new cash, AssemblyAI looks to grow its AI-as-a-service business/TechCrunch

Our team

We're growing at breakneck speed and are hiring across all departments. Help us research and deploy advanced machine learning and deep learning technologies, and serve them to developers at startups and Fortune 500s through a simple API.
Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the training of reinforcement learning (RL) algorithms. However, evaluating the fitness of such representations requires training RL algorithms which is computationally intensive and has high variance outcomes. To alleviate this issue, we design an evaluation protocol for unsupervised RL representations with lower variance and up to 600x lower computational cost.

Light-weight probing of unsupervised representations for Reinforcement Learning/AssemblyAI & NYU

Working at AssemblyAI

Join our fully remote, global team of researchers and engineers.

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Staff Deep Learning Researcher, Speech
We're searching for a Staff Deep Learning Researcher to join our team, to continually push the state-of-the-art in speech recognition and NLP.
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Software Engineer, Python
We are growing quickly, and we're searching for a driven and dedicated Software Engineer to work on our multi-functional team.
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