AI Research Engineer (Kernel & Inference Optimization)
Tasks
- Analyze latency and throughput
- Build scalable inference pipelines
- Create testing datasets and deployment scenarios
- Design model serving architectures
- Develop benchmarking methodologies
- Identify bottlenecks in serving pipelines
- Improve inference infrastructure through experimentation
- Improve memory utilization
- Integrate inference solutions into production
- Monitor performance metrics
- Optimize deployment strategies
- Optimize inference frameworks
- Optimize inference latency
- Optimize inference throughput
- Profile inference performance
- Run inference experiments in simulated and production environments
Perks/Benefits
- Autonomy
- Continuous learning
- Flexible location
- Fully remote
- Professional development
- Technical ownership
Skills/Tech-stack
Benchmarking | C++ | CUDA | Docker | Inference Optimization | Kubernetes | Latency optimization | Machine Learning | Memory Optimization | NVIDIA TensorRT | ONNX | Performance Monitoring | Profiling | PyTorch | Python | TensorFlow | Throughput Optimization
Education
N/A
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