AI Research Technical Program Manager — Hybrid

Advanced Micro Devices, Inc.; Santa Clara (CA); Hybrid; USD 190,000 - 240,000; Full time

Job summary

Advanced Micro Devices, Inc. is seeking a Technical Program Manager, AI Research to drive execution across AI research scientists and infrastructure engineers advancing reinforcement learning and post training for LLMs. You will translate scientific agendas into executable plans with milestones, resources, and criteria.

You will coordinate across research, engineering, and hardware teams so experiments are reproducible, interpretable, and scalable toward internal adoption.

Qualifications

• Experience in technical program management or research program management in AI/ML or large-scale compute organizations.
• Ability to surface schedule risk, surface research plans, and coordinate across scientists and infra engineers.
• Familiarity with publication cycles and internal tech report processes.

Responsibilities

• Own research program planning: milestones, roadmaps, publication timelines, dependency tracking.
• Coordinate research execution with multiple AI teams on datasets, eval harnesses, compute allocation.
• Maintain research operating cadence: milestone reviews, experiment readouts, decision logs, blockers.
• Track progress with metrics: benchmark movement, reproducibility, generalization, governance checkpoints.
• Manage compute planning and experiment throughput: GPU scheduling, cluster capacity, prioritization.

Skills

Program management

Cross-functional leadership

Research coordination

ML/RL familiarity

Stakeholder communication

Education

Bachelor’s degree in Computer Science/Engineering or related field

Job description

Advanced Micro Devices, Inc. is seeking a Technical Program Manager, AI Research to drive execution across AI research scientists and infrastructure engineers advancing reinforcement learning and post training for LLMs. You will translate scientific agendas into executable plans with milestones, resources, and criteria.

You will coordinate across research, engineering, and hardware teams so experiments are reproducible, interpretable, and scalable toward internal adoption.