AI Software Development Eng.

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career. The Role: The TrainingAtScale team at AMD is looking for a Training Optimization Engineer to help build and optimize our large-scale training infrastructure on AMD GPUs. This is an engineering-focused role centered around improving the performance, stability, and scalability of distributed training systems. You will work closely with internal model and platform teams to push the boundaries of generative AI model training. Key Responsibilities: • Participate in the development and maintenance of AMD’s internal training framework. • Optimize distributed training pipelines and parallelism strategies (Data Parallelism, Tensor Parallelism, Pipeline Parallelism, ZeRO, etc.). • Improve communication scheduling and kernel overlap to reduce training latency and maximize GPU utilization. • Tune the performance of core operators using HIP/CUDA and low-level profiling tools. • Integrate and adapt open-source training frameworks such as Megatron-LM, TorchTitan, DeepSpeed, etc. • Support internal model training workloads with performance, reliability, and scalability improvements. • Collaborate across teams to investigate and resolve system-level bottlenecks in large-scale training. Preferred Qualifications: • Solid engineering background and familiarity with end-to-end deep learning training workflows. • Hands-on experience with training framework internals (e.g., Megatron-LM, TorchTitan, DeepSpeed, FairScale). • Strong debugging and performance analysis skills (profiling, tracing, etc.). • Understanding of distributed training techniques such as data parallelism, tensor parallelism, pipeline parallelism, ZeRO optimization. • Excellent communication and cross-functional collaboration skills. Bonus Points: • Experience with large-scale model training (e.g., LLMs, ViTs, MoE). • Hands-on experience with CUDA or HIP kernel development. • Familiarity with communication libraries such as NCCL/RCCL and techniques like kernel overlap. • Prior involvement in high-performance ML infrastructure projects.#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. The Role: The TrainingAtScale team at AMD is looking for a Training Optimization Engineer to help build and optimize our large-scale training infrastructure on AMD GPUs. This is an engineering-focused role centered around improving the performance, stability, and scalability of distributed training systems. You will work closely with internal model and platform teams to push the boundaries of generative AI model training. Key Responsibilities: • Participate in the development and maintenance of AMD’s internal training framework. • Optimize distributed training pipelines and parallelism strategies (Data Parallelism, Tensor Parallelism, Pipeline Parallelism, ZeRO, etc.). • Improve communication scheduling and kernel overlap to reduce training latency and maximize GPU utilization. • Tune the performance of core operators using HIP/CUDA and low-level profiling tools. • Integrate and adapt open-source training frameworks such as Megatron-LM, TorchTitan, DeepSpeed, etc. • Support internal model training workloads with performance, reliability, and scalability improvements. • Collaborate across teams to investigate and resolve system-level bottlenecks in large-scale training. Preferred Qualifications: • Solid engineering background and familiarity with end-to-end deep learning training workflows. • Hands-on experience with training framework internals (e.g., Megatron-LM, TorchTitan, DeepSpeed, FairScale). • Strong debugging and performance analysis skills (profiling, tracing, etc.). • Understanding of distributed training techniques such as data parallelism, tensor parallelism, pipeline parallelism, ZeRO optimization. • Excellent communication and cross-functional collaboration skills. Bonus Points: • Experience with large-scale model training (e.g., LLMs, ViTs, MoE). • Hands-on experience with CUDA or HIP kernel development. • Familiarity with communication libraries such as NCCL/RCCL and techniques like kernel overlap. • Prior involvement in high-performance ML infrastructure projects.#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.