About the Role
The role involves building and maintaining infrastructure for machine learning workflows, enabling smooth transition from model development to live deployment while ensuring performance, scalability, and reliability.
Responsibilities
- Design and implement CI/CD pipelines tailored for machine learning models
- Automate deployment processes for ML models in production environments
- Monitor model performance and system health in real time
- Collaborate with data scientists to operationalize trained models
- Ensure models meet latency and throughput requirements
- Maintain version control for models, data, and code
- Optimize resource utilization for training and inference workloads
- Troubleshoot issues across development, staging, and production systems
- Implement model rollback and A/B testing frameworks
- Enforce security and compliance standards in ML pipelines
- Integrate models with backend services and APIs
- Manage containerized environments using Docker and Kubernetes
- Scale infrastructure to support growing ML workloads
- Develop tools for model validation and data quality checks
- Support reproducibility of experiments and training runs
- Work with distributed computing frameworks as needed
- Document system architecture and operational procedures
- Respond to incidents related to model serving infrastructure
- Collaborate with engineering teams on system reliability
- Stay current with advancements in ML infrastructure tools
- Ensure logging and observability across ML components
- Help define best practices for model monitoring
- Contribute to capacity planning for ML systems
- Improve feedback loops between production data and training
- Assist in cost optimization of cloud-based ML resources
Nice to Have
- Master’s degree in computer science or related field
- Experience with TensorFlow, PyTorch, or similar frameworks
- Background in data engineering or DevOps roles
- Knowledge of feature store implementations
- Familiarity with model registry solutions
- Experience with serverless ML deployments
- Contributions to open-source MLOps projects
- Understanding of regulatory requirements for ML systems
- Exposure to edge deployment of ML models
Compensation
Competitive salary with equity package and performance bonuses
Work Arrangement
Hybrid work model with flexibility for remote work across USA and Israel
Team
Cross-functional team focused on delivering robust machine learning systems in a collaborative, agile environment
About the Role
This position supports the end-to-end deployment and management of machine learning models, focusing on automation, reliability, and scalability. Engineers will work closely with research and product teams to bring models from prototype to production.
Technology Stack
The team uses Kubernetes for orchestration, Docker for containerization, and leverages cloud platforms for scalable compute. Tools include GitLab CI, Prometheus, Grafana, and various open-source MLOps frameworks.
Visa sponsorship available for qualified candidates in applicable regions
