Large scale, high performance speech recognition for large communication platforms and enterprises (that means: phone calls and meetings). We're the fastest, most technically oriented, frontier pushing speech company in the world and enjoy working with people who continue to push that boundary.
AI Engineer San Francisco, CA, United States or Remote
Devops Engineer San Francisco, CA, United States
Product Engineer San Francisco, CA, United States
Software Engineer San Francisco, CA, United States
We've increased revenue 2x in the last year and are fully funded for growth by good investors (YC, Slack, Nvidia, more forthcoming).
We've completely rewritten the speech stack to be truly smart and scalable based on insights from our physics background building particle and astrophysics experiments.
We're considerably more efficient — tens to hundreds of times — and more scalable, which means we have significant adoption at large scale platforms you've heard of.
We have a pretty flat engineering team: everyone has the opportunity to contribute to every project. For the most part, our engineers work directly on customer-specific requirements and feature enhancements — often working with the customers -- so they can immediately see the fruits of their labors. We frequently have projects that engineers can take considerable, or even complete, ownership of. Our team communicates in person, over Slack, and via ad hoc video meetings.
We have built the very fastest and most scalable speech analytics engine ever made. This means that we need to handle incredible amounts of audio per hour, and still remain responsive and fault-tolerant. Doing this robustly requires careful design of the APIs, the high-performance computing backends, and the company-owned GPU hardware in our datacenter.
You would be working on creating new, advanced speech analytics, such as sentiment analysis processing that fuses textual transcripts with tone of voice. These analytics would be run against many thousands of hours of audio daily and on real-time voice streams.
You would be developing our open source tools, managing the project and engaging the community.
You would be identifying, and then implementing, data services for feeding new types of audio data into our machine learning pipelines.
We thrive on deep tech, and we get stoked when engineers can find great ways to intuitively communicate these complex ideas to users, improve the performance of our innermost loops and business logic, and help couple customer needs to engineering resources. Customer-driven features provide plenty of opportunity for fast-paced, fresh projects.
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