Senior Machine Learning Engineer, ML Infrastructure - Online
Tasks
- Build model deployment workflows
- Design and operate online inference infrastructure
- Implement autoscaling and self healing
- Implement canary testing and A/B experimentation
- Improve observability of online ML systems
- Lead architectural improvements for reliability scalability and cost efficiency
- Optimize inference performance
- Optimize model packaging and deployment automation
- Serve production machine learning models with low latency
- Split traffic and perform rollback
- Validate and monitor model performance in production
Perks/Benefits
- Commute subsidy
- Competitive retirement pension plans
- Employee resource groups
- Employee stock ownership
- Generous vacation
- Global employee assistance program
- Mental health and wellbeing programs
- Training and development
- Volunteering and donation matching
Skills/Tech-stack
A/B | A/B Testing | Autoscaling | B testing | Canary testing | Cost monitoring | Distributed Systems | Dynamic batching | Error Rate Monitoring | Error rate | GPU Acceleration | GPU Kernel | GPU kernel optimization | Google Kubernetes | Google Kubernetes Engine | Inference Optimization | Inference Server | Kernel optimization | Kubernetes | Kubernetes Engine | Latency optimization | Machine Learning | Model Deployment | Model compilation | Monitoring | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | Observability | PyTorch | Python | Quantization | Rate monitoring | Ray Serve | Request Scheduling | Rollback | Runtime tuning | TensorFlow Serving | Throughput Optimization | Torchserve | Traffic splitting | Triton Inference Server
Education
N/A
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