ML Infrastructure Engineer
Tasks
- Apply quantization and model distillation
- Build dataset storage and metadata indexing
- Build low latency inference pipeline
- Build model training infrastructure
- Collaborate with researchers to translate needs into infrastructure
- Design multimodal data ingestion and validation pipelines
- Develop research tooling for debugging visualization and experiment analysis
- Implement model sharding and activation checkpointing
- Log metrics and monitor training workloads
- Maintain research codebase for fast iteration
- Optimize GPU utilization and training throughput
- Optimize dataloaders sharding and prefetching
- Reason about performance memory I O and GPU utilization
- Scale distributed training across GPU clusters
- Schedule training jobs and manage checkpoints
- Use model compilation for inference optimization
Perks/Benefits
- N/A
Skills/Tech-stack
Activation checkpointing | CUDA | Data Pipelines | Data Validation | Dataloader optimization | Dataset Management | Distributed Training | Flatbuffers | GPU Optimization | Knowledge Distillation | Kubernetes | Low Latency | Low-Latency Systems | Metadata indexing | Model Quantization | Model compilation | Prefetching | Protobuf | PyTorch | Python | Serialization | Sharding | Slurm | Video Processing
Education
N/A
Regions
Countries
States
Cities
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