Research Scientist in AI Foundation Model Infrastructure

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

• Design and build scalable infrastructure for large-scale model training, evaluation, and inference.
• Optimize distributed training systems across compute, memory, and communication.
• Improve system reliability, efficiency, and observability for large-scale workloads.
• Develop frameworks for evaluation, data processing, and model lifecycle management.
• Co-design systems and algorithms to improve performance of foundation models.

• Currently pursuing a PhD in computer science, mathematics, engineering, or a related field, with an expected graduation date in 2027 and the ability to commit to an onboarding date by the end of 2027.
• Excellent coding ability, data structures, and fundamental algorithm skills, proficient in C/C++ or Python, etc.
• Experience in distributed systems, large-scale training infrastructure, or ML systems.
• Familiarity with deep learning frameworks and system optimization.

• Strong problem-solving and engineering skills.
• Strong communication and collaboration skills.