About the Role
The role involves building and maintaining scalable machine learning systems that power personalization, recommendations, and automation features within a customer engagement platform. The engineer will work closely with cross-functional teams to integrate models into production environments and improve system reliability and performance.
Responsibilities
- Design and implement scalable machine learning pipelines
- Develop infrastructure for real-time model inference
- Optimize model training and deployment workflows
- Collaborate with data scientists to productionize models
- Improve monitoring and observability of ML systems
- Ensure models meet performance and latency requirements
- Work on feature stores and data pipelines for ML
- Support A/B testing frameworks for model evaluation
- Maintain high availability of ML services
- Troubleshoot production issues related to machine learning components
- Contribute to data validation and model quality checks
- Integrate third-party data sources for model training
- Enhance model versioning and rollback capabilities
- Collaborate on defining ML metrics and KPIs
- Support model retraining and automated refresh cycles
- Work on scalable storage solutions for ML artifacts
- Improve developer tooling for ML workflows
- Ensure compliance with data privacy standards
- Participate in system architecture reviews
- Mentor junior engineers on ML best practices
- Evaluate new technologies for ML infrastructure
- Document system designs and operational procedures
- Contribute to incident response for ML outages
- Drive improvements in model explainability
- Support platform scalability for growing customer base
Nice to Have
- Master’s degree in computer science or related field
- Experience with large-scale recommendation systems
- Background in real-time personalization engines
- Knowledge of natural language processing
- Experience with online learning systems
- Familiarity with MLOps practices
- Contributions to open-source ML projects
- Experience with feature store implementations
- Understanding of causal inference methods
- Exposure to privacy-preserving ML techniques
Compensation
Competitive salary, equity, and benefits package
Work Arrangement
Hybrid or remote options available
Team
Collaborative environment with data science, engineering, and product teams focused on advancing machine learning capabilities
About the Team
The team builds core machine learning infrastructure used across the platform to deliver personalized customer experiences. Engineers focus on scalability, reliability, and speed of deployment for ML-powered features.
What You’ll Achieve
- Ship production-grade machine learning systems that serve millions of users.
- Improve model deployment frequency and reduce time-to-market.
- Increase confidence in model performance through robust testing.
- Enable data scientists to iterate faster with better tooling.
Visa sponsorship available for qualified candidates
