Staff Machine Learning Engineer, ML Infrastructure - Online
Tasks
- Automate model packaging and deployment workflows
- Build and improve model serving systems
- Build autoscaling and self healing capabilities
- Design online inference infrastructure for low latency and reliability
- Develop model deployment and validation infrastructure
- Implement dynamic batching and request scheduling
- Implement traffic splitting and rollback
- Improve observability for online ML systems
- Lead architectural improvements for scalability and cost efficiency
- Optimize inference performance
- Set up canary testing and A B experimentation
Perks/Benefits
- Commute subsidy
- Disability insurance
- Employee stock ownership
- Generous vacation
- Health insurance
- Life insurance
- Mental health & wellbeing programs
- Retirement plan
- Training and development programs
Skills/Tech-stack
A/B | A/B Experimentation | Autoscaling | Caching | Canary testing | Deployment Automation | Distributed Systems | Dynamic batching | Error Rate Monitoring | Error rate | GKE | GPU Acceleration | GPU Kernel | GPU kernel optimization | Google Kubernetes | Google Kubernetes Engine | Inference Server | Kernel optimization | Kubernetes | Kubernetes Engine | Latency optimization | Model Validation | Model compilation | Monitoring | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | Observability | PyTorch | Python | Quantization | Rate monitoring | Ray | Ray Serve | Rollback | Runtime tuning | TensorFlow Serving | Throughput Optimization | Torchserve | Traffic splitting | Triton Inference | Triton Inference Server
Education
N/A
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