Software Engineer - Model Performance

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.