Technical Leader: AI Systems Architecture

Technical Leader – AI Systems Architecture

We are building a new research and engineering lab focused on advancing AI systems architecture for large-scale multimodal models. We are looking for a senior technical leader to drive innovation at the intersection of multimodal foundation models, scalable training/inference systems, and next-generation computing platforms.

Role Overview

You will lead research and system-level development of large multimodal AI models, working closely with researchers, systems engineers, and platform teams. The role emphasizes both hands-on technical depth and architectural leadership, from model design to large-scale deployment.

Core Expertise

• Deep expertise in multimodal learning, with hands-on experience training and scaling:

  • Vision–Language Models (VLMs)

  • Video or temporal models

  • Multimodal or agent-based systems

• Strong understanding of transformer architectures, diffusion models, and large multimodal model (LMM) inference

• Demonstrated contributions to well-known open-source AI projects, such as:

  • General-purpose model libraries

  • Multimodal or vision–language frameworks

  • Large-scale training or diffusion-based systems

Technical Skills

• Strong proficiency in PyTorch and/or JAX

• Experience with large-scale distributed training and inference, including model, data, and pipeline parallelism

• Solid understanding of performance optimization for AI systems (memory efficiency, latency, throughput)

• Proven ability to build end-to-end training and inference pipelines, including:

  • Data preprocessing and curation

  • Distributed training and evaluation

  • Inference optimization and deployment

• Experience collaborating within open-source communities

  • Maintainer or core contributor experience is a plus

Research & Innovation

• Publication record in top-tier AI conferences (e.g., NeurIPS, ICLR, CVPR, ICCV, ACL, EMNLP) strongly preferred

• Demonstrated ability to propose, prototype, and validate novel architectures or algorithms

• Strong interest in bridging research innovation and real-world system deployment

Qualifications

• PhD in Computer Science, Computer Engineering, or a related field

• 8+ years of experience in AI research, systems engineering, or applied machine learning

• Strong English communication and technical documentation skills

• Comfortable working in cross-functional and interdisciplinary teams