MTS, Developer Experience

Amazon.com, Inc.•San Francisco, CA

51d

About The Position

Are you interested in a unique opportunity to advance the accuracy and efficiency of Artificial General Intelligence (AGI) systems? If so, you're at the right place! We are the AGI Autonomy organization, and we are looking for a driven and talented Member of Technical Staff to join us to build state-of-the art agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We're entering an exciting new era where agents can redefine what AI makes possible. We'd love for you to join our lab and build it from the ground up! The team is shaping developer experience from the ground up. Building tools that enable researchers to move at the speed of thought: IDEs that seamlessly shell out to supercomputers, CI/CD pipelines that orchestrate thousands of agentic commands simultaneously, and build systems optimized for GPU-accelerated workflows. Your infrastructure will be the foundation that enables the next generation of AI research, directly contributing to our mission of building the most capable agents in the world.

Requirements

5+ years of experience in DevOps, release engineering, or developer tools/infrastructure
Expertise with shell scripting and command-line tools (bash, zsh, etc.)
Experience managing CI/CD systems such as AWS CodePipeline, Jenkins, CircleCI, or similar platforms
Hands-on experience managing code repositories and version control systems (GitLab, GitHub, Phabricator, etc.)
Proficiency in at least one programming language (Python, Go, Rust, or similar) for automation and tooling development
Experience building and maintaining developer tooling or infrastructure at scale
Strong understanding of containerization (Docker, containerd) and container orchestration

Nice To Haves

Experience with release management and maintaining large-scale software deployments
Knowledge of container build internals (Docker multi-stage builds, BuildKit, layer caching optimization)
Experience working with GPU infrastructure and CUDA development workflows
Background in IDE development or customization (VSCode extensions, JetBrains plugins, etc.)
Experience building development tools for machine learning or data science teams
Knowledge of ML frameworks (PyTorch, TensorFlow) and their build/dependency requirements
Experience with AWS developer tools and services (CodeBuild, CodeDeploy, CodeCommit, etc.)

Responsibilities

Design and implement a modern, fast, and ergonomic development environment for AI researchers, eliminating current pain points in build times, testing workflows, and iteration speed
Build and manage CI/CD pipelines (CodePipeline, Jenkins, etc.) that support large-scale AI research workflows, including pipelines capable of orchestrating thousands of simultaneous agentic experiments
Develop tooling that bridges local development environments with remote supercomputing resources, enabling researchers to seamlessly leverage massive compute from their IDEs
Manage and optimize code repository infrastructure (GitLab, Phabricator, or similar) to support collaborative research at scale
Implement release management processes and automation to ensure reliable, repeatable deployments of research code and models
Optimize container build systems for GPU workloads, ensuring fast iteration cycles and efficient resource utilization
Work directly with researchers to understand workflow pain points and translate them into infrastructure improvements
Build monitoring and observability into development tooling to identify bottlenecks and continuously improve developer experience
Design and maintain build systems optimized for ML frameworks, CUDA code, and distributed training workloads