MLOps Research Engineer
Tasks
- Automate reporting for benchmarking and experiment comparison
- Build digital infrastructure for computational workloads
- Build scientific scaffolding for active learning and closed loop optimization
- Create data ETL pipelines for model workflows
- Define CI/CD pipelines
- Design distributed training and inference frameworks
- Develop deployment frameworks for production grade systems
- Establish ML engineering best practices for code hygiene testing and reproducibility
- Implement experiment tracking and model management
- Implement model registries and evaluation frameworks
- Mentor computational scientists on software engineering workflows
- Orchestrate resources with Kubernetes Ray and Dagster
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Active Learning | Automated reporting | Azure | CI/CD | Closed Loop | Closed-loop optimization | Cloud platform | Dagster | Data ETL | Distributed Computing | Experiment tracking | Google Cloud | Google Cloud Platform | JAX | Kubernetes | Loop Optimization | MLflow | Model Management | PyTorch | Python | Ray | Version control | Weights and Biases
Education
N/A
Regions
Countries
States
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