Lead Machine Learning Engineer
Tasks
- Architect training and inference infrastructure
- Build self healing infrastructure for reliability
- Define and enforce SLIs SLOs
- Design Kubernetes based inference servers
- Establish CI CD model versioning and canary deployment standards
- Lead distributed training implementation and optimization
- Manage GPU memory and throughput optimization
- Mentor engineers in system design and operations
- Optimize GPU cost and performance
- Optimize inference serving layer for low latency
- Own training and inference technical roadmap
Perks/Benefits
- 401k plan
- Disability benefits
- Life insurance
- Medical/Dental/Vision
- PTO
- Paid time off
- Tuition assistance
Skills/Tech-stack
Anyscale | Auto Scaling | C++ | CI/CD | CUDA | Canary deployments | Distributed Systems | GPU Architecture | GPU Utilization | GPU memory | GPU memory management | Grafana | Inference Server | Infra-as-code | Kubernetes | MLOps | MLflow | Machine Learning | Memory Management | Model versioning | Multi-cloud | 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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