Machine Learning Engineer — Training Optimization
Tasks
- Build training infrastructure checkpointing fault tolerance reproducible workflows
- Collaborate with research teams on training architectures
- Define and monitor training performance metrics
- Evaluate and integrate training optimization techniques
- Improve distributed training strategies
- Optimize model training pipelines
- Profile systems and resolve training performance bottlenecks
- Translate research concepts into production systems
- Tune training optimizers learning rate schedulers batch sizes numerical precision
Perks/Benefits
- Collaborative technical team
- Continuous learning
- Equity opportunities
- Fast feedback loops
- High ownership
- Opportunity to work on cutting-edge AI
- Technical quality focus
Skills/Tech-stack
AMD GPUs | BF16 | Backpropagation | Checkpointing | Data parallelism | Data-parallel | Deep learning | DeepSpeed | Distributed Training | FP16 | FP8 | FSDP | Fault Tolerance | Fully Sharded Data Parallel | GPU Computing | Gradient Checkpointing | Learning Rate | Learning Rate Scheduling | Machine Learning | Megatron | Model Parallelism | NVIDIA GPUs | Numerical precision | Optimization Algorithms | Pipeline parallelism | Profiling | PyTorch | Reproducibility | Zero
Education
N/A
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