Machine Learning Engineer — Training Optimization
Tasks
- Build training infrastructure with checkpointing and fault tolerance
- Define monitor and improve training performance metrics
- Evaluate and integrate advanced training techniques
- Improve distributed training approaches
- Optimize large scale model training pipelines
- Profile systems to identify performance bottlenecks
- Translate research concepts into production ready systems
- Tune training optimizers and learning rate schedulers
Perks/Benefits
- Equity opportunities
- Fast feedback loops
- Flexible schedule
- Remote work
- Technical learning opportunities
Skills/Tech-stack
BF16 | Backpropagation | Checkpointing | Data parallelism | Deep learning | DeepSpeed | Distributed Training | FP16 | FP8 | FSDP | Fault Tolerance | GPU Computing | GPU Training | Gradient Checkpointing | Learning Rate | Learning Rate Scheduling | Machine Learning | Megatron | Model Parallelism | Multi-GPU | Multi-GPU Training | Multi-node | Multi-node Training | Numerical precision | Optimization Algorithms | Pipeline parallelism | Profiling | PyTorch | Zero
Education
N/A
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