About the Role
The role involves building and maintaining infrastructure for machine learning workflows, ensuring data reliability, scalability, and efficient model deployment. The engineer will work closely with cross-functional teams to streamline data processing and improve system performance.
Responsibilities
- Design and implement data pipelines for large-scale machine learning applications
- Develop and maintain MLOps frameworks to support model deployment and monitoring
- Ensure data quality and integrity across processing stages
- Optimize data storage and query performance in cloud environments
- Collaborate with data scientists to operationalize machine learning models
- Automate workflows for training, validation, and inference pipelines
- Monitor system performance and troubleshoot production issues
- Improve data security and access controls
- Integrate data from multiple sources into unified platforms
- Support reproducibility and versioning of data and models
- Build tools for data validation and anomaly detection
- Work with distributed systems and stream processing technologies
- Maintain documentation for data architectures and processes
- Participate in code reviews and system design discussions
- Contribute to incident response and on-call rotations
Nice to Have
- Master’s degree in a technical field
- Experience with real-time data streaming technologies
- Background in statistics or data science
- Contributions to open-source data or ML projects
- Familiarity with feature store systems
- Experience in regulated industries with data compliance needs
Compensation
Competitive salary and equity package
Work Arrangement
Remote within the US
Team
Small, fast-moving team focused on data infrastructure and machine learning operations
Why This Role Matters
This position plays a critical role in bridging data science and engineering, ensuring models move efficiently from development to production. The engineer will directly impact the reliability and scalability of ML-driven products.
Tech Stack
Primary tools include Python, GCP, Kubernetes, Airflow, and Terraform. The team uses modern MLOps practices with an emphasis on automation, testing, and monitoring.
Not available
