具身智能 AI Infra 工程师
Tasks
- Build training orchestration
- Create experiment management and observability system
- Deploy model inference services
- Design training platform architecture
- Develop data pipelines
- Implement training data management system
- Improve engineering standards and infrastructure quality
- Manage model versions and artifacts
- Optimize low latency inference on simulation and hardware
- Perform code reviews
- Set up parallel reinforcement learning training pipeline
- Support sim to real data loop
- Track training metrics and compare experiments
- Write architecture and deployment documentation
Perks/Benefits
- N/A
Skills/Tech-stack
Algorithms | C# | C++ | CI/CD | Data Structures | Design Patterns | Distributed Training | Docker | GPU Programming | Git | Inference Optimization | Kubernetes | MLOps | MLflow | NCCL | ONNX Runtime | PPO | PyTorch | Python | Ray | Real Time | Real-time Inference | Real-time inference optimization | Reinforcement Learning | Ring AllReduce | SAC | TensorRT | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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