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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Software Engineer, Data Plane - **Company:** Amazon.com, Inc. - **Location:** Cupertino, CA, United States - **Experience:** Experienced - **Salary:** $165,200.0 - $223,600.0 - **Contract:** Permanent contract - **Skills:** Board Bringup, Artificial Intelligence, C++ (Programming Language), Code Review, Extract Transform Load (ETL), Distributed Systems, Memory Management, Machine Learning, Open Source Technology, Remote Direct Memory Access, Tensorflow, Software Engineering, Network Switches, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Model Validation, Parallel Computation, Optimization Algorithms, Build Process, TensorRT, Software Coding, Software Version Control - **Published:** August 4, 2026 - **Apply:** https://www.amazon.jobs/en/jobs/10491191/ml-software-engineer-data-plane ## About the Role Bachelor's degree or equivalent - 4+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Knowledge of computer architecture, operating systems, and parallel computing - Knowledge of Linux fundamentals - Strong proficiency in C/C++ - Experience developing compute kernels for GPUs, DSPs, or custom accelerators - Proven track record of owning and delivering complex software features end-to-end, Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques - Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT - Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware - Experience with distributed systems - collective communication, RDMA, or high-speed interconnect programming - Experience with hardware simulation environments and model validation workflows - Demonstrated early adopter of AI-assisted development tools - uses LLMs or code-generation agents as part of daily workflow ## Description The MLIL DataPlane team is looking for a Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration. Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models. This is a ground-up effort with rapidly evolving hardware and software. We are looking for an individual contributor who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack. Key job responsibilities - Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference. - Implement and validate LLM architectures end-to-end - from PyTorch model definition through distributed execution on custom hardware. - Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism. - Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets. - Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bring-up. - Own features end-to-end: from design through implementation, testing, and integration into the broader software stack. - Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [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) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [From Model to Metal: An Open Source Stack for Accelerating Intelligence](https://www.wearedevelopers.com/videos/1636-from-model-to-metal-an-open-source-stack-for-accelerating-intelligence) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)