David AI is looking to build and scale the core infrastructure that powers their cutting-edge audio ML products, enabling researchers and engineers to train, deploy, and evaluate machine learning models efficiently.
Requirements
- 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.
- Experience with feature stores, experiment tracking (MLflow, Weights and Biases), or custom CI/CD pipelines.
Responsibilities
- 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.
Other
- Proven ability to thrive in fast-moving startup environments.
