AI Agent Engineer

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. Responsibilities Build and integrate multi-agent workflows; expose platform capabilities via standard APIs and agent protocols (e.g., MCP/A2A). Improve training reliability (automation, failover, health checks) to keep jobs running through cluster faults. Optimize distributed training performance (parallelism, comms/storage/operator tuning) to improve GPU utilization. Own observability & debugging (logs/metrics/traces, profiling, visualization). Requirements Strong software + systems engineering; experience with large-scale/distributed training. Familiar with a multi-agent framework (e.g., CrewAI, LangGraph) or a training stack (e.g., Megatron/DeepSpeed). Proven performance profiling and bottleneck isolation skills. Protocol & security awareness for agent connectivity/governance. Nice to Have SFT/RL fine-tuning experience. End-to-end agent + LLM training pipeline experience. TorchFT (or similar) fault-tolerant training experience. ACADEMIC CREDENTIALS: Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent #LI-FL1 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. This posting is for an existing vacancy.

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. This posting is for an existing vacancy.

Responsibilities Build and integrate multi-agent workflows; expose platform capabilities via standard APIs and agent protocols (e.g., MCP/A2A). Improve training reliability (automation, failover, health checks) to keep jobs running through cluster faults. Optimize distributed training performance (parallelism, comms/storage/operator tuning) to improve GPU utilization. Own observability & debugging (logs/metrics/traces, profiling, visualization). Requirements Strong software + systems engineering; experience with large-scale/distributed training. Familiar with a multi-agent framework (e.g., CrewAI, LangGraph) or a training stack (e.g., Megatron/DeepSpeed). Proven performance profiling and bottleneck isolation skills. Protocol & security awareness for agent connectivity/governance. Nice to Have SFT/RL fine-tuning experience. End-to-end agent + LLM training pipeline experience. TorchFT (or similar) fault-tolerant training experience. ACADEMIC CREDENTIALS: Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent #LI-FL1