Join our AI team in France and move AI out of the lab and into the hands of millions!
Many companies are building AI products these days, but few have a mission worth getting behind.
We’re working hard to empower 450 million deaf and hard-of-hearing people in their everyday life. Working at Ava means making an actual difference to people every day. Our users need our product to be the best it can be every day.
That’s why we need you to help us move AI from prototype to product. Our product integrates cutting-edge research in diarization as well as humans-in-the-loop, making for a diverse and challenging but also very rewarding set of tasks. Being able to build a clever AI algorithm is not the same as building a working, well engineered system though. If you take pride in building efficient, maintainable software under demanding domain constraints, and want to bring your creativity to the table, making the right trade-offs (and refusing the wrong ones) then we’d love to have you on board!
← All Open Positions and More About Ava • Role • Ava • Apply
The tech stack we use at Ava is not a secret. In fact, most of it is fairly standard, it’s more what we do with it than what tools we use to do the job that makes us special. Feel free to reach out to us to ask questions about what we use and how; we want you to know exactly what you’re getting into. Talk to our engineers, they’re a cool bunch! 🙂
To be clear: this is a description of where we are, not where we want to be, or where we’ll stay. If you immediately start thinking of obvious ways to make this better: great, let’s compare notes!
So without further ado, here’s how our service currently runs, in very broad outline:
REST & Websocket API endpoints in NodeJS express, overwhelmingly TypeScript
We still have a few any casts in there. They’ve been becoming rare though. 😄
Further-back backend (so... the not client-facing one) also in Node/TS that joins together the multiple audio streams for each conversation.
Scientific backend (this is where the ML/DL happens) in python with both tensorflow and pytorch elements.
👆🏽These three components pass messages via Redis, the whole thing is pretty much event-driven. You have been warned. 🙂
Storage in Firebase, MongoDB, S3,
Logging and monitoring in Elastic/Kibana
Infrastructure hosted on AWS, with use of ECS, EB, SQS, mostly managed with Terraform.
Jenkins for CI/CD