Matterport - Senior ML Ops Engineer
Tasks
- Analyze machine learning model performance
- Apply model optimization techniques
- Automate model optimization workflows
- Build CI/CD pipelines for models
- Deploy models into production
- Design experiments for accuracy impact
- Develop model execution libraries
- Ensure scalability and reliability
- Identify performance bottlenecks
- Improve MLOps practices and infrastructure
- Integrate optimized models into platform
- Monitor models in production
- Optimize inference speed
- Reduce resource consumption
Perks/Benefits
- Commuter benefits
- Employee resource groups
- Employee stock purchase plan
- Healthcare coverage
- Life insurance
- Mental health counseling
- Paid time off
- Retirement plan matching
- Tuition reimbursement
Skills/Tech-stack
AWS | Airflow | Architecture Search | Azure | CD pipelines | CI/CD | CI/CD pipelines | Cloud deployment | Cloud platform | Containerization | Continuous Deployment | Continuous integration | Deep learning | Deploying machine learning models | Docker | Git | Google Cloud | Google Cloud Platform | Inference Optimization | Knowledge Distillation | Kubeflow | Kubernetes | Learning models | Machine Learning | Machine Learning Models | Model Compression | Model Monitoring | Model Optimization | Model profiling | Neural Architecture | Neural Architecture Search | Neural Networks | Pruning | PyTorch | Python | Quantization | Temporal | TensorFlow | Workflow Orchestration
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
Regions
Countries
States
Cities
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