Live subtitles for any conversation. enjoy 24/7 access to conversations around you
Why this role?
You - someone with great programming skills and ideally prior exposure to Machine Learning. The core of your mission will be to build and maintain the pipeline involved to transcribe real world group conversations (multi-devices). This includes backend development (NodeJS, Python, MongoDB) to handle and process data from devices.
The signal is acquired via an array of ad-hoc microphones, and is processed to guess who says what, using a set of techniques: source localization, voice recognition, microphones calibration, speech recognition, source separation… all in real time.
If you are a developer interested to improve your data skills, Ava will provide you with a great environment to have a real impact.
Our stack: Node.JS, Python, AWS, Docker, Redis, MongoDB, React, jenkins, Terraform.
Let's chat! Especially if you're interested to:
- Actually change lives at an unprecedented scale: How often do you hear about apps that make people cry of joy? It is really for us a unique opportunity and privilege to be able to meaningfully improve the lives of 100,000s of people - and yet to still be at the very beginning of our mission!
- Work with some of the best people in the world: We have an incredibly talented and passionate team that is a lot of fun to work with. We're still super small and have accomplished some things that were thought impossible!
- Tackle our most interesting and impactful problems: Our AI team is still small, and people wear many hats. You'd jump between machine-learning models, infrastructure, internal tools, process — participating in every phase from inception to implementation. Absolutely no boredom.
- Join us at an incredible time: We're well-funded and hit product-market fit, which gives us a huge green field to work with. You'd join at the perfect time to shape what we build and how we grow, so we can create a more inclusive world.
So what will you do as a Data Engineer at Ava?
- You'll own designing and scaling Ava's AI infrastructure, supporting mission-critical algorithms for our users.
- You'll help improve the performance, reliability and monitoring of our cutting-edge AI systems — such as improving the speed and responsiveness of our deep neural network.
- You'll work with the AI team to build and maintain robust internal machine-learning tools and processes with a focus on the data pipeline. Think of projects such as integrating supervised data at scale to our models, a critical project to improve the accuracy of Ava.
- You'll own the deployment processes and best practices to ensure the coordination of the AI team internally and in coordination with Ava's CTO.
- You'll help find, vet and lead a team of data engineers and scientists to support the AI team goals.
You'd be perfect for this role if:
- You have experience scaling production software and machine learning models. You've worked on a product experiencing rapid growth and understand what needs to be done to scale to the next level.
- You can readily learn most technologies as you go. To you, technologies are about tools and tradeoffs, not an ideology.
- You care about the strategic and business implications of anything you build. You're not just going after cool stuff — you understand the balance between craft, speed, and the bottom line.
- You've spent meaningful time (3+ years) as an engineer or tech lead — or even better, you've managed a team before and have experience with agile development.
- Bonus: You're fluent with NodeJS, Python, noSQL and React.
- Bonus: You have experience in building deep learning models, especially speech recognition and natural language processing systems.
Meet your team
Ava Accessibility at a glance
Ava Accessibility focuses on Mobile, SaaS, Healthcare, Health Care Information Technology, and Artificial Intelligence. Their company has offices in San Francisco and Paris. They have a small team that's between 11-50 employees. To date, Ava Accessibility has raised $1.845M of funding; their latest round was closed on November 2016.