Software Engineer, Machine Learning Infrastructure

About the role

About our Engineering team

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.

About this role

As a Software Engineer, Machine Learning Infrastructure at David AI, you will build and scale the core infrastructure that powers our cutting-edge audio ML products. You’ll be leading the development of the systems that enable our researchers and engineers to train, deploy, and evaluate machine learning models efficiently.

In this role, you will

- Design and maintain data pipelines for processing massive audio datasets, ensuring terabytes of data are managed, versioned, and fed into model training efficiently.

- Develop frameworks for training audio models on compute clusters, managing cloud resources, optimizing GPU utilization, and improving experiment reproducibility.

- Create robust infrastructure for deploying ML models to production, including APIs, microservices, model serving frameworks, and real-time performance monitoring.

- Apply software engineering best practices with monitoring, logging, and alerting to guarantee high availability and fault-tolerant production workloads.

- Translate research prototypes into production pipelines, working with ML engineers and data teams to support efficient data labeling and preparation.

- Evaluate and integrate new MLOps technologies and optimization techniques to enhance infrastructure velocity and reliability.

Your background looks like

- 5+ years of backend engineering with 2+ years ML infrastructure experience.

- Hands-on experience scaling cloud infrastructure and large-scale data processing pipelines for ML model training and evaluation.

- Proficient with Docker, Kubernetes, and CI/CD pipelines.

- Proven ML model deployment and lifecycle management in production.

- Strong system design skills optimizing for scale and performance.

- Proficient in Python with deep Kubernetes experience.

Bonus points if you have

- Experience with feature stores, experiment tracking (MLflow, Weights and Biases), or custom CI/CD pipelines.

- Familiarity with large-scale data ingestion and streaming systems (Spark, Kafka, Airflow).

- Proven ability to thrive in fast-moving startup environments.

Some technologies we work with

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

Compensation and benefits