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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Software Engineer- AI/ML, AWS Neuron... - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $193,300.0 - $261,500.0 - **Contract:** Internship / Graduate position - **Skills:** Artificial Intelligence, Amazon Web Services, Application Frameworks, Code Review, Computer Programming, Data Mining, Software Design Patterns, Distributed Computing Environment, Distributed Systems, Information Retrieval, Machine Learning, Natural Language Processing, Open Source Technology, Tensorflow, Software Engineering, High Performance Computing, Pytorch, Deep Learning, Information Technology, Build Process, Hardware Infrastructure, Software Coding, Software Version Control, Programming Languages - **Published:** August 11, 2026 - **Apply:** https://www.juju.com/job/00000000gmohxh ## About the Role Bachelor's degree in computer science or equivalent - 5+ years of non-internship professional software development experience - 5+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Experience as a mentor, tech lead or leading an engineering team - Experience in machine learning, data mining, information retrieval, statistics or natural language processing Preferred Qualifications - Master's degree in computer science or equivalent - Experience in computer architecture - Previous software engineering expertise with Pytorch/Jax/Tensorflow, Distributed libraries and Frameworks, End-to-end Model Training. ## Description The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium, Amazon's custom machine learning accelerator. Neuron includes an ML compiler, runtime, collectives library, and application framework that integrate with PyTorch and JAX, so customers can train frontier-scale models on Trainium without rewriting their stack. The Distributed Training team is at the forefront of training a wide range of models on AWS's custom ML accelerators, supporting novel architectures while maximizing their training performance. Working across the stack from PyTorch and JAX down to the hardware and software boundary, our engineers build the infrastructure that large-scale training depends on, develop new parallelism and numerics techniques, and tune high-performance kernels for the operations that dominate a training step, so every compute unit is doing useful work on our customers' most demanding workloads. We combine deep hardware knowledge with ML expertise to push the limits of training efficiency at scale. As part of the broader Neuron organization, our team works across multiple technology layers, from frameworks and kernels through to the compiler, runtime, and collectives teams. This is hardware and software co-design in practice. A single throughput gap rarely sits in one layer, so tracing it means following the problem across the stack, deciding where the fix belongs, and working with the team that owns that layer to land it. We not only optimize current performance but also contribute to future architecture designs, since the gaps we characterize today become requirements for the next generation of Trainium. We work closely with customers to enable their models and ensure they train efficiently. This role offers a rare opportunity to work at the intersection of machine learning, high-performance computing, and distributed systems, where you will help shape the direction of AI acceleration technology. You will architect and implement business critical features, and mentor a team of experienced engineers. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We are inventing. We are experimenting. It is a genuinely unique learning culture. The team works directly with customers on model enablement, providing hands-on support and optimization expertise so their training workloads reach the performance they need on AWS ML accelerators. We also collaborate with the open source ecosystem, contributing upstream so integration is seamless and performance holds at scale for customers and developers., You will lead efforts to optimize distributed training performance on Trainium, with a primary focus on training throughput, model FLOPs utilization, and time to convergence across the Neuron software stack. You will work across PyTorch, JAX, and the Neuron compiler and runtime to enable and tune large-scale training workloads on the latest Trainium instances. You will bring up model architectures that have never run on Trainium, identifying the missing operators, sharding strategies, and numerics needed to train them correctly, and then close the gap between correct and fast. You will own the parallelism strategies these models depend on, spanning data, tensor, pipeline, expert, and context parallelism, and apply reduced-precision formats where they measurably pay off. You will profile end to end to determine whether a workload is bound by compute, memory, collectives, or host overhead, then drive the fix to the layer that owns it, working with compiler, runtime, and collectives engineers to land it. You will translate the performance gaps you characterize into requirements that influence future Trainium architecture, and contribute upstream to the open source frameworks our customers train on. ## Related Videos - [The Software Engineer 2030: From Coder To AI Orchestrator? - Patrick Schnell](https://www.wearedevelopers.com/videos/1825-the-software-engineer-2030-from-coder-to-ai-orchestrator-patrick-schnell) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Are Code Reviews Worth It? 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