AI Research Engineer (Kernel & Inference Optimization)
Tasks
- Build scalable inference pipelines for cloud edge and resource constrained environments
- Collaborate to integrate optimized inference solutions into production
- Create testing datasets and deployment scenarios for real world challenges
- Design optimize model serving architectures for low latency and high throughput
- Develop benchmarking methodologies and performance validation frameworks
- Identify serving pipeline bottlenecks including batch network and memory inefficiencies
- Improve inference infrastructure through experimentation and iteration
- Monitor analyze performance metrics including latency throughput memory and error rates
- Optimize inference frameworks and deployment strategies for scalability and resilience
- Run controlled inference experiments and evaluate system performance
Perks/Benefits
- Continuous learning and professional development
- Flexible location options
- Fully remote
- High technical ownership
Skills/Tech-stack
AI infrastructure | Artificial Intelligence | Benchmarking | Bottleneck analysis | Cloud Computing | Distributed Systems | Edge Computing | Inference Optimization | Latency optimization | Machine Learning | Memory Optimization | Model Deployment | Model Serving | Multi-Modal | Multi-modal Machine Learning | Performance Monitoring | Performance Tuning | Profiling | Systems Performance Tuning | Systems performance | Testing | Throughput Optimization | Tokenization
Education
N/A
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