Systems Research Engineer
Tasks
- Architect distributed system components for AI workloads
- Build multi tenant low latency fault tolerant AI serving frameworks
- Collaborate with multidisciplinary stakeholders
- Communicate research progress and evaluation results
- Design heterogeneous memory scheduling
- Develop resource orchestration and scheduling
- Evaluate scalability across CPU GPU NPU clusters
- Implement distributed system modules
- Implement high throughput inference serving
- Optimize KV cache management
- Profile and tune large scale inference and data pipelines
- Research cache sharing data locality techniques
- Translate research ideas into publications
Perks/Benefits
- N/A
Skills/Tech-stack
AI infrastructure | C++ | Data Locality | Distributed Systems | Fault Tolerance | Heterogeneous Memory Scheduling | Heterogeneous memory | Inference Serving | KV cache | LLM serving | Learning systems | Load Balancing | Machine Learning | Machine learning systems | Memory scheduling | Operating Systems | Performance Tuning | Profiling | PyTorch distributed | Python | Ray Serve | Resource orchestration | Resource scheduling | State management | TGI | TensorRT-LLM | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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