WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. **Together, we advance your career. **
THE ROLE:
The AI/ML Frameworks team is hiring an Software Development Engineer to build and maintain scalable DevOps infrastructure that accelerates AMD’s AI software development.
You will design and own CI/CD pipelines, manage Kubernetes‑based GPU environments, and automate systems using Python, Go, and Ansible. The role involves creating and maintaining production‑grade automation and tooling that enables fast, reliable software delivery across teams.
THE PERSON:
The ideal candidate is a skilled DevOps/infrastructure engineer with strong programming abilities. They write clean, maintainable code in Python or Go, and can navigate ML framework source code (PyTorch, TensorFlow, ROCm) to debug issues, optimize build processes, or contribute fixes. They have solid knowledge of build systems and toolchains—understanding how CMake, Bazel, and compiler toolchains work is critical for effective issue triaging and root cause analysis. They are proficient in Kubernetes, CI/CD tools, and infrastructure automation frameworks such as Ansible. Familiarity with C++ is valuable for navigating lower-level framework components. This person thrives in collaborative, fast-paced environments, can drive technical execution with minimal oversight, and is passionate about knowledge sharing and upleveling their team.
KEY RESPONSIBILITIES:
- Build System Expertise & Issue Triaging: Develop deep expertise in build tools and flows (CMake, Bazel, Make, compiler toolchains). Triage complex build failures by understanding the full build pipeline—from source to binary. Identify root causes across infrastructure, toolchain, and code-level issues.
- Team Training & Knowledge Sharing: Train and mentor team members on build systems, CI/CD workflows, and debugging techniques. Create documentation, runbooks, and training sessions to ensure the team can effectively triage issues independently. Foster a culture of continuous learning around build infrastructure.
- ML Framework Integration & Code Contribution: Understand the architecture and codebase of ML frameworks (PyTorch, TensorFlow, ROCm stack). Review, debug, and contribute code changes as needed to resolve build issues, improve CI reliability, or support new features.
- Tooling & Automation Development: Design and develop internal tools, automation scripts, and services primarily in Python and Go. Write well-tested, production-grade code to solve infrastructure and workflow challenges.
- CI/CD Pipeline Development: Design, implement, and manage efficient continuous integration and delivery pipelines using Buildkite, GitHub Actions, and Jenkins to enable rapid and reliable software deployment for ML workloads.
- Kubernetes Infrastructure Management: Deploy and maintain robust Kubernetes-based environments across both on-premise and cloud platforms to support scalable service orchestration.
- Infrastructure Automation: Automate provisioning, configuration, and management of infrastructure using Ansible, Python, and Bash to improve system consistency and reduce manual intervention.
- Service Deployment with Helm: Administer application and service deployment in Kubernetes using Helm charts for consistent and repeatable release processes.
- GPU Server Support: Configure, manage, and maintain GPU-based compute environments including lifecycle automation and hardware-level test integration for ML training and inference workloads.
- Database and Observability Integration: Interact with MySQL databases to support dynamic data updates and integrate data sources into Grafana dashboards for monitoring and insights.
- Cross-Functional Collaboration: Work closely with ML framework developers, SREs, and project stakeholders to ensure system-level alignment and high-impact delivery.
- Quality Assurance Enablement: Integrate automated testing frameworks into CI pipelines to ensure code quality, stability, and performance across development cycles.
PREFERRED EXPERIENCE:
- Build Systems & Toolchains: Strong understanding of CMake, Bazel, Make, and compiler toolchains (GCC, Clang, LLVM). Ability to debug complex build failures, understand dependency resolution, and optimize build performance.
- Programming Languages: Strong proficiency in Python and Go for building tools, services, and automation. The ability to read and modify C++ code is a plus for working with ML framework internals and build configurations.
- ML Framework Familiarity: Understanding of ML framework architecture (PyTorch, TensorFlow, JAX, or similar). Ability to navigate large codebases, understand their build systems, and contribute fixes or improvements.
- Mentorship & Training: Experience documenting complex systems and training team members. Ability to break down technical concepts and create effective learning materials.
- DevOps Tools & Automation: Proficient with Buildkite, GitHub Actions, Jenkins, Ansible, and scripting for streamlining DevOps workflows.
- Containerization & Orchestration: Strong experience with Docker, Kubernetes, and Helm for deploying and managing scalable, containerized applications.
- Infrastructure as Code (IaC): Hands-on experience automating infrastructure provisioning and configuration to ensure reproducibility and scalability across environments.
- GPU-Based Compute Environments: Familiarity with GPU server lifecycle management, ROCm/CUDA toolchains, and integration of GPU resources into CI test workflows for performance-critical ML applications.
- Monitoring & Observability: Experience using tools like Checkmk, Prometheus, and Grafana to monitor infrastructure health and application performance.
- Version Control & Collaboration: Advanced knowledge of Git-based version control, including branching strategies and CI/CD integration for collaborative development.
- Linux & System Administration: Solid background in Linux environments, including shell scripting and system-level troubleshooting across distributed systems.
- Agile & Cross-Disciplinary Collaboration: Comfort working in Agile teams and partnering with software, infrastructure, and product teams to drive consistent delivery and innovation.
ACADEMIC CREDENTIALS:
- Bachelor's or Master's degree in Computer Science, Software Engineering, or related technical discipline.
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_Benefits offered are described: _AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
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This posting is for an existing vacancy.