Lead Machine Learning Engineer
Tasks
- Architect training infrastructure
- Build autoscaling for inference
- Build self-healing infrastructure
- Define and enforce SLIs and SLOs
- Deploy canary releases
- Design Kubernetes based inference servers
- Implement and optimize Ray AnyScale for distributed training
- Implement model versioning
- Improve GPU utilization
- Mentor ML engineers on system design and operations
- Monitor and scale ML models
- Optimize GPU memory management
- Own training and inference technical roadmap
- Set ML deployment standards
Perks/Benefits
- 401k plan
- Dental insurance
- Disability benefits
- Life insurance
- Medical insurance
- Paid time off
- Tuition assistance program
- Vision insurance
Skills/Tech-stack
Anyscale | Auto Scaling | C++ | CI/CD | CUDA | Canary Deployment | GPU Architecture | Grafana | Inference Server | Infra-as-code | Kubernetes | MLOps | MLflow | Machine Learning | Model versioning | Prometheus | Pulumi | Python | Ray | SLI | SLO | Terraform | Torchserve | Triton Inference | Triton Inference Server | VLLM | Weights and Biases | “as-code”
Education
N/A
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