About the Role
The role involves building and maintaining backend systems that power machine learning applications, ensuring reliability, scalability, and performance across production environments.
Responsibilities
- Design and maintain backend services that support machine learning workflows
- Develop APIs to connect machine learning models with production systems
- Optimize data pipelines for efficient model training and inference
- Collaborate with data scientists to integrate models into scalable environments
- Monitor system performance and troubleshoot production issues
- Implement automated testing for backend components
- Ensure data consistency and integrity across distributed systems
- Work on database design and query optimization
- Support deployment and monitoring of ML-powered features
- Contribute to architectural decisions for long-term scalability
- Maintain documentation for backend systems and processes
- Participate in code reviews and technical discussions
- Improve system security and access controls
- Assist in capacity planning for growing data demands
- Integrate third-party tools and services into the backend stack
- Refactor legacy code to improve maintainability
- Collaborate on incident response and post-mortem analysis
- Ensure compliance with data privacy standards
- Evaluate new technologies for backend and ML infrastructure
- Mentor junior engineers on best practices and system design
Nice to Have
- Master’s degree in a technical field
- Experience with TensorFlow or PyTorch deployment
- Background in data engineering or ML operations
- Contributions to open-source software
- Experience with model monitoring and retraining pipelines
- Familiarity with feature store systems
- Knowledge of A/B testing frameworks
- Experience in high-growth startup environments
- Previous work on low-latency inference systems
- Understanding of model explainability tools
Compensation
$120,000 - $160,000 per year, commensurate with experience
Work Arrangement
Remote with flexible hours; some team overlap with Central Time zone required
Team
Collaborative engineering team focused on data-intensive applications and ML integration
Tech Stack
- Primary languages: Python, Go
- Cloud infrastructure: Google Cloud Platform
- Container orchestration: Kubernetes
- Data storage: PostgreSQL, BigQuery
- ML deployment: Vertex AI, custom model servers
Growth Opportunities
- Chance to lead technical initiatives
- Regular participation in architecture design
- Access to training and conference budgets
- Opportunities to mentor junior team members
- Involvement in recruiting and onboarding
Available for qualified candidates
