Machine Learning Platform Lead Engineer, Training and Inference
Tasks
- Architect training and inference infrastructure
- Build self healing infrastructure for cluster reliability
- Define and enforce SLIs SLOs
- Define model versioning and rollout strategies
- Design and maintain Kubernetes inference servers
- Establish CI CD standards for ML deployments
- Implement Canary deployments
- Implement MLOps pipelines
- Lead distributed training with Ray
- Manage GPU memory optimization for inference
- Mentor ML platform and applied ML engineers
- Monitor and scale ML services
- Optimize GPU utilization and inference latency
- Own AnyScale governed training compute layer
Perks/Benefits
- 401k plan
- Disability benefits
- Generous paid time off
- Life insurance coverage
- Medical/Dental/Vision
- PTO
- Tuition assistance
- Tuition assistance program
Skills/Tech-stack
Anyscale | Autoscaling | C++ | CI/CD | CUDA | Canary Deployment | Distributed Systems | GPU Architecture | GPU memory | GPU memory management | Grafana | Inference Server | Infra-as-code | Kubernetes | MLOps | MLflow | Machine Learning | Memory Management | Model versioning | Prometheus | Pulumi | Python | Ray | SLI/SLO | Terraform | Torchserve | Triton Inference | Triton Inference Server | VLLM | Weights and Biases | “as-code”
Education
N/A
Regions
Countries
States
Cities
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