Backend Software Engineer

At David AI, our engineers build the pipelines, platforms, and models that transform raw audio into high-signal data for leading AI labs and enterprises. We're a tight-knit team of product engineers, infrastructure specialists, and machine learning experts focused on building the world's first audio data research company.

We move fast, own our work end-to-end, and ship to production daily. Our team designs real-time pipelines handling terabytes of speech data and deploys cutting-edge generative audio models.

As a Backend Engineer at David AI, you'll build the systems and infrastructure that power how our users process and make sense of the audio data they'll use to train their models, working closely with researchers to consistently iterate on how to best collect our data.

Design and build scalable backend systems and core data processing pipelines that derive actionable insights from terabytes of audio data every day.

Architect and maintain the APIs and services that power features thousands of users interact with daily.

Build, deploy, and evaluate LLM and DSP based solutions to increase our customers' understanding of nuanced features across our datasets.

Iterate rapidly on our research hypotheses by working closely with researchers and our Operations team to deploy backend infrastructure that supports new data collection.

Stay up-to-date with cutting edge technologies and frameworks to apply to our current product offering across backend engineering, data engineering, machine learning, and signal processing.

2+ years of backend engineering experience.

Strong backend fundamentals with experience designing scalable systems, APIs, and data pipelines that handle significant scale and volume.

A proven track record of delivering engineering solutions that contributed value to customers.

Experience working in a high-pace environment where constant progress was enabled by detail-oriented execution.

Emphasis on building robust, reliable infrastructure that supports highly polished, production-grade products.

AI/ML or audio experience is not required but you should be excited to learn.

Educational or professional experience with digital signal processing and a deep understanding of speech.

Experience building and deploying ML models in a production environment.

Led (managed or tech-led) engineering teams in high-growth environments.

Node.js, tRPC, PostgreSQL, AWS, Trigger.dev , WebRTC, FFmpeg, TypeScript.