Machine Learning Systems Engineer
Tasks
- Build and maintain cloud infrastructure on AWS Azure
- Build production ML infrastructure across GPU and cloud
- Build scalable training and inference pipelines
- Deploy and manage ML workloads on Kubernetes
- Design build operate ML inference systems
- Design build operate ML training systems
- Develop GPU accelerated ML workloads
- Develop model deployment monitoring evaluation and production operations
- Develop tooling and automation for ML experimentation and deployment
- Implement distributed systems for high volume model inference
- Optimize infrastructure for diffusion based language models
- Optimize model inference for latency throughput memory cost
- Translate model architectures into production systems
Perks/Benefits
Skills/Tech-stack
AWS | Azure | CUDA | Distributed Systems | Docker | GPU Computing | Inference Optimization | Kubeflow | Kubernetes | ML orchestration | Model Serving | ONNX Runtime | PyTorch | Python | SGLang | TensorFlow | TensorRT | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
Cities
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