About the Role
The ideal candidate will bridge data engineering and machine learning operations by building robust data pipelines, supporting model deployment, and ensuring system reliability and scalability.
Responsibilities
- Design and implement data pipelines for large-scale data processing
- Develop and maintain infrastructure for machine learning model deployment
- Ensure data accuracy, reliability, and timely delivery across systems
- Collaborate with data scientists to operationalize machine learning models
- Monitor and optimize performance of data workflows and ML systems
- Support version control and reproducibility for ML models and datasets
- Implement CI/CD practices for data and ML pipelines
- Work with cloud-based platforms to manage data storage and compute resources
- Enforce data governance, security, and compliance standards
- Troubleshoot and resolve issues in production data environments
- Document architecture, processes, and system changes
- Participate in code reviews and system design discussions
- Contribute to capacity planning for data infrastructure
- Automate routine data operations and monitoring tasks
- Integrate data from multiple sources into unified systems
- Support real-time and batch data processing workflows
- Optimize data models for query performance and scalability
- Evaluate and integrate new data and ML tools and frameworks
- Ensure system observability through logging and alerting
- Collaborate with software engineers to align data systems with product needs
- Maintain up-to-date knowledge of MLOps best practices
- Assist in defining standards for data quality and pipeline testing
- Support disaster recovery and backup strategies for data systems
- Participate in on-call rotations for production support
- Drive improvements in system reliability and efficiency
Nice to Have
- Master’s degree in a technical field
- Experience with MLOps platforms like MLflow or Kubeflow
- Hands-on work with real-time data streaming systems
- Knowledge of distributed systems architecture
- Prior work in fast-paced technology environments
- Exposure to A/B testing or experimentation platforms
- Experience supporting high-availability services
- Background in audio or digital media technology
- Familiarity with regulatory compliance in data handling
Compensation
Competitive salary with benefits
Work Arrangement
Hybrid work model
Team
Collaborative engineering team focused on data systems and machine learning operations
Why Join Us?
- We offer a dynamic work environment where innovation and collaboration drive our technology solutions.
- You’ll work with talented engineers on cutting-edge data and machine learning systems.
- Opportunities for professional growth and technical leadership are supported and encouraged.
Our Commitment to Inclusion
- We value diversity and are dedicated to fostering an inclusive workplace for all team members.
- All qualified applicants will receive consideration without regard to race, gender, religion, or other protected attributes.
Available for qualified candidates
