### **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.
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