Software Engineer - Model Performance
Software Engineer optimizes ML model inference performance using techniques like quantization and speculative decoding. Requires backend experience with PyTorch, TensorRT, CUDA, and deep GPU knowledge for LLMs.
About the job
Responsibilities
- Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure.
- Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vllm, sglang, CUDA, and other libraries to debug ML performance issues.
- Apply and scale optimization techniques across a wide range of ML models, particularly large language models.
- Collaborate with a diverse team to design and implement innovative solutions.
- Own projects from idea to production.
Requirements
- Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.
- Experience with one or more general-purpose programming languages, such as Python or C++.
- Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching).
- Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM.
- Demonstrated interest and experience in LLMs.
- Deep understanding of GPU architecture.
Bonus
- Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs).
- Experience with CUDA or similar technologies.
- Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions.
- Experience with Docker and Kubernetes.
Skills
PyTorch, TensorRT, Tensorrt-Llm, CUDA, Python, C++, vLLM, Sglang, Docker, Kubernetes
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