AI Systems Engineer – AI Model (Training & Inference)
Tasks
- Build observability and automated analysis tooling
- Build quantization pipelines
- Build training infrastructure job orchestration distributed checkpointing data loading storage optimization
- Collaborate with silicon architecture teams on software feedback
- Contribute to ROCm ecosystem and developer experience
- Debug distributed training issues
- Design validation and testing infrastructure proxy workloads synthetic benchmarks
- Develop monitoring alerting and compliance infrastructure
- Enable and optimize large scale model training
- Optimize RCCL collective communication patterns
- Optimize inference serving frameworks
- Own end to end model execution stack
- Write and optimize GPU kernels for training and inference
Perks/Benefits
- N/A
Skills/Tech-stack
AWQ | Anomaly Detection | Attention | Continuous batching | DeepSpeed | Distributed Training | FP4 | FP6 | FP8 | FSDP | GEMM | GPTQ | GPU Kernel | GPU kernel optimization | HIP | KV cache | Kernel optimization | Kubernetes | Log Analysis | MLIR | Megatron-LM | Performance baselining | PyTorch | Quantization | RCCL | Regression Detection | SGLang | Speculative decoding | Synthetic Benchmarks | Torchserve | Triton | VLLM
Education
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