About the Role
The role involves building and managing the infrastructure that supports machine learning workflows in advertising systems, focusing on automation, monitoring, and continuous integration and delivery pipelines for ML models.
Responsibilities
- Design and implement scalable ML pipelines for advertising applications
- Maintain and optimize CI/CD systems for machine learning models
- Monitor model performance and system reliability in production
- Collaborate with data scientists to operationalize machine learning models
- Ensure infrastructure meets security and compliance standards
- Troubleshoot and resolve issues in ML deployment workflows
- Improve model deployment speed and efficiency
- Integrate monitoring and alerting tools for ML systems
- Support version control and reproducibility of ML experiments
- Work with containerization and orchestration technologies such as Docker and Kubernetes
- Automate testing procedures for machine learning components
- Manage infrastructure as code using configuration management tools
- Optimize resource utilization for training and inference workloads
- Ensure data integrity and pipeline consistency
- Collaborate with software engineers to integrate ML models into applications
- Develop documentation for ML systems and processes
- Participate in code and design reviews
- Stay current with advancements in MLOps and related technologies
- Support root cause analysis for production incidents
- Contribute to capacity planning for ML infrastructure
- Implement access controls and authentication for ML services
- Work with cloud platforms to host and scale ML systems
- Ensure efficient data flow between data sources and ML models
- Support A/B testing and model experimentation frameworks
- Promote best practices in ML engineering across teams
Nice to Have
- Master's degree in a technical field
- Experience with large-scale ML deployment in production
- Contributions to open-source MLOps projects
- Certifications in cloud platforms or DevOps
- Experience with feature store implementations
- Knowledge of model explainability tools
- Background in advertising technology platforms
- Familiarity with regulatory compliance for data use
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Remote with flexible hours
Team
Collaborative engineering team focused on machine learning systems
Technology Stack
- Primary languages: Python, Bash
- Containerization: Docker
- Orchestration: Kubernetes
- Cloud platforms: AWS and GCP
- CI/CD tools: Jenkins, GitHub Actions
- Monitoring: Prometheus, Grafana
- Data processing: Apache Airflow, Spark
- Model serving: TensorFlow Serving, TorchServe
Growth Opportunities
- Access to professional development budgets
- Opportunities to lead technical initiatives
- Mentorship programs for career advancement
- Regular tech talks and knowledge sharing sessions
Available for qualified candidates
