Senior ML Engineer (Token Factory)
Tasks
- Analyze GPU performance bottlenecks
- Build distributed inference systems
- Build distributed training systems
- Design inference engines
- Develop custom kernels
- Develop low precision inference pipelines
- Develop low precision training pipelines
- Drive inference optimization
- Implement sharding strategies
- Implement speculative decoding
- Implement testing and CI/CD
- Maintain production ML systems
- Optimize hardware aware performance
- Optimize kv cache
- Profile GPU workloads
Perks/Benefits
- Career development
- Collaborative innovation culture
- Continuous learning
- Flexible work environment
- High autonomy and ownership
- International diverse team environment
Skills/Tech-stack
Attention Mechanism | CI/CD | CUDA | Custom Kernels | Deep learning | Distributed Training | Distributed inference | FP8 | Flash Attention | GPU Architecture | GPU Profiling | Inference Optimization | KV cache | Language Model | Large Language Model | Low Precision | Low-precision training | MXFP4 | Machine Learning | Memory hierarchy | Nsight | PyTorch | PyTorch Profiler | Python | Quantization | ROPE | Sharding | Speculative decoding | Transformer | Unit Testing | Version control
Education
N/A
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