Job Description

My Client:

My client is an AI technology company building next-generation foundation models, intelligent agents, and AI-native systems. The team is pushing beyond traditional model development by exploring AutoResearch - enabling AI systems to autonomously discover, optimize, and evolve future large language model architectures.

This role sits at the intersection of LLM research, AutoML, hardware-software co-design, and large-scale model training. You will work on some of the most ambitious challenges in AI today: teaching AI to improve AI.

Job Responsibilities:

Design and develop AutoResearch systems capable of autonomously discovering and optimizing next-generation LLM architectures.
Build AI-driven research workflows using neural architecture search (NAS), evolutionary algorithms, automated experimentation, or LLM-powered research agents.
Develop efficient proxy evaluation frameworks and scaling methodologies to predict large-scale model performance.
Collaborate with systems, compilers, and hardware teams to co-optimize model architectures under real-world compute, memory, and latency constraints.
Participate in large-scale foundation model pre-training, validation, and architecture iteration.
Stay at the forefront of research in AutoML, foundation models, scaling laws, and hardware-aware AI systems.

Job Requirements:

Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics etc. strong background in Deep Learning, LLMs, Transformer architectures, or Foundation Model research.
Experience with model training, scaling, optimization, or architectural design.
Familiarity with AutoML, Neural Architecture Search (NAS), evolutionary optimization, reinforcement learning, or automated research systems is highly preferred.
Good understanding of distributed training, GPU architectures, memory systems, or large-scale AI infrastructure.
Strong programming skills in Python and modern deep learning frameworks such as PyTorch, JAX, DeepSpeed, or Megatron-LM.
Publications in top-tier AI/ML conferences (NeurIPS, ICML, ICLR, MLSys, etc.) are highly valued.

What They Offer:

Opportunity to work on frontier AI research where AI systems help design the next generation of AI models.
Highly research-driven environment with opportunities for publications, patents, and long-term technical impact.
Competitive compensation and strong growth opportunities as the team scales globally.