Amazon Software Engineer - AI/ML AWS Neuron Inference Multimodal 

United States, Washington, Seattle 

873999910

The ML Inference team works side by side with chip architects, compiler engineers and runtime engineers to create, build and optimize distributed inference solutions with Trainium/Inferentia instances. Experience with training and optimizing inference on these large models using Python/C++ is a must. Model parallelization, quantization, memory optimization - vLLM, DeepSpeed and other distributed inference libraries can be central to this and extending all of them for the Neuron based system is the key.Key job responsibilities
You will help leading the efforts to build and achieve the best distributed training and inference performance of PyTorch, JAX, TensorFlow with XLA and other advanced frameworks on Neuron stacks. You will help optimizing these models to ensure the highest performance and maximize the efficiency of them running on the custom AWS Trainium and Inferentia silicon and the Trn1, Inf1/2 servers. Strong software development (Python and C++) and Machine Learning knowledge (Multimodal, Computer Vision, Speech) are both critical to this role.
Work/Life BalanceMentorship & Career Growth

- Bachelor's degree in computer science or equivalent
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing

- Master's degree in computer science or equivalent
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience in computer architecture
- Previous software engineering expertise with Pytorch/Jax/Tensorflow, Distributed libraries and Frameworks, End-to-end Model Training.
- Previous experience with training multi-modal models for understanding and generating images/videos/audios