About the Role
The role involves developing and maintaining machine learning platforms, integrating models into production environments, and working closely with cross-functional teams to deliver intelligent software solutions.
Responsibilities
- Design and implement scalable machine learning pipelines
- Collaborate with data scientists to productionize ML models
- Optimize model inference performance and latency
- Build APIs and services for ML-driven applications
- Ensure reliability and observability of ML systems
- Write clean, maintainable, and well-tested code
- Improve data ingestion and preprocessing workflows
- Support deployment and monitoring of ML models
- Work on distributed computing frameworks for training
- Contribute to architectural decisions for ML infrastructure
- Evaluate and integrate new ML libraries and tools
- Troubleshoot production issues in real-time systems
- Maintain documentation for ML components
- Participate in code reviews and technical design discussions
- Drive best practices in model versioning and reproducibility
- Enhance security and access controls for ML services
- Scale backend systems to handle increasing model loads
- Collaborate on feature engineering pipelines
- Ensure compliance with data privacy standards
- Optimize resource utilization in cloud environments
- Support A/B testing frameworks for model evaluation
- Work with infrastructure teams on CI/CD pipelines
- Improve model monitoring and alerting systems
- Contribute to technical roadmap planning
- Mentor junior engineers on ML best practices
Nice to Have
- Master’s degree in Computer Science or related field
- Experience with MLOps platforms
- Background in natural language processing
- Familiarity with large-scale data pipelines
- Knowledge of model explainability techniques
- Experience with real-time inference systems
- Contributions to open-source ML projects
- Prior work in startup environments
- Understanding of edge deployment for ML
- Experience with model drift detection
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid work model with flexible remote options
Team
Collaborative engineering team focused on scalable ML systems
About the Team
The team builds end-to-end machine learning platforms that enable rapid experimentation and deployment. Engineers work across the full stack to create tools that simplify model integration and monitoring for data science teams.
Technology Stack
Primary languages include Python and JavaScript. Infrastructure runs on Kubernetes with services deployed via Docker. The stack integrates PostgreSQL, Redis, Kafka, and cloud-native ML tools for scalable processing.
Available for qualified candidates
