### **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](http://Trigger.dev), WebRTC, FFmpeg.
### **Compensation and benefits**
* Rapid career growth at one of the fastest growing Series A companies, within a new and booming industry.
* Competitive salary and equity package.
* Flexible PTO policy.
* Top-notch health, dental, and vision coverage with 100% company reimbursement for most plans.
* Paid lunch and dinner in the office, every day through DoorDash.
* 401k access.
Visa sponsorship is offered for this role.
