Tasks:
- * Automate data preparation, cleaning, annotation, and versioning
- * Build high-performance data orchestration and loading systems
- * Design and build cloud computing platforms
- * Develop frontend management consoles and backend distributed services
- * Develop model training and data production infrastructure
- * Optimize GPU cluster scheduling and elastic resource management
- * Orchestrate containerized training and inference workloads
Perks/Benefits:
Skills/Tech stack required:
[AI Agents] [Batch Processing] [C++] [Cloud Native] [Cloud-native infrastructure] [Cluster scheduling] [Data orchestration] [Data Pipelines] [DeepSpeed] [Distributed Systems] [Flink] [FSDP] [Function Calling] [Go] [GPU Cluster] [GPU cluster scheduling] [Java] [Kubernetes] [MCP] [Megatron] [Model Inference] [Model Training] [Parquet] [Python] [Ray Data] [React] [Sandbox execution] [Spark] [Stream processing] [TensorRT] [VLLM] [Vue]
Educational requirements:
[Master's Degree]
Role(s):
[AI Platform Engineer] [Cloud Infrastructure Engineer] [Distributed Systems Engineer] [Engineer] [Full Stack Engineer] [Infrastructure Engineer] [Platform Engineer] [Stack Engineer] [Systems Engineer]