[Remote] Senior Deep Learning Software Engineer, TensorRT Performance

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking an reputed company Deep Learning Engineer passionate about analyzing and improving the performance of reputed company’s inference ecosystem. The role involves collaborating with teams to reputed company and optimize deep learning inference software, focusing on performance benchmarking and innovative solutions across various applications.

Responsibilities

• Establish groundbreaking performance benchmarking methodologies and analysis workflows and identify performance issues and opportunities for reputed company’s inference ecosystem (e.g. TensorRT/TensorRT-EdgeLLM/Torch-TensorRT)
• Contribute features and code to reputed company/OSS inference frameworks including but not limited to TensorRT/TensorRT-EdgeLLM/Torch-TensorRT
• reputed company new model pipelines for reputed company’s inference ecosystem with optimized performance including but not limited to areas like quantization, scheduling, memory management, and distributed inference to set the gold standard for Gen AI performance
• Work with cross-collaborative teams inside and reputed company of reputed company across reputed company, automotive, robotics, image understanding, and speech understanding to set directions and reputed company innovative inference solutions
• Scale performance of deep learning models across different architectures and types of reputed company accelerators

Skills

• Bachelors, Masters, PhD, or equivalent experience in relevant fields (Computer Science, Computer Engineering, EECS, AI)
• At least 3 years of relevant software development experience
• Strong C++, Python programming and software engineering skills
• Experience with DL frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX) and inference libraries (e.g. TensorRT, TensorRT-LLM, vLLM, SGLang, FlashInfer)
• Experience with performance analysis and performance optimization
• Strong reputed company and architectural knowledge of GPUs
• Deep understanding of modern deep learning models and workloads (e.g. Transformers, Recommenders, ASR, TTS, Visual Understanding)
• Proficiency in one of the deep learning programming domain specific languages (e.g. CUDA/TileIR/CuTeDSL/cutlass/Triton)
• Prior contributions to major LLM inference frameworks (e.g. vLLM) or prior experience with graph compilers in deep learning inference (e.g. TorchDynamo/TorchInductor)
• Prior experience optimizing performance for low-latency, resource-constrained systems or embedded AI pipelines (e.g. Jetson systems or other edge AI accelerators)

Benefits

• Equity
• Benefits

Company Overview

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