Machine Learning Engineer (Training Optimization)

TL;DR

Machine Learning Engineer (Training Optimization) (AI/ML): Designing and optimizing large-scale distributed training systems for multimodal and foundation models with an accent on compute, memory, and communication layer performance. Focus on implementing custom CUDA/Triton kernels and leveraging frameworks like Megatron-LM and NeMo to unlock scalability.

Location: Beijing, China

Company

hirify.global is building a creative intelligence engine to power the future of AI-assisted design through foundational technologies and large-scale models.

What you will do

• Design, implement, and optimize large-scale machine learning systems for training.
• Improve GPU utilization, communication overhead, and memory efficiency.
• Partner with research and modeling teams to align systems with algorithmic needs.
• Apply industry-leading frameworks and best practices for distributed training.
• Develop custom CUDA or Triton kernels for low-level optimization.
• Debug, profile, and fine-tune training workflows to maximize scalability.

Requirements

• Strong background in LLMs, multimodal AI, or diffusion models.
• Proficiency in Python; familiarity with C++ or Rust is a plus.
• Deep knowledge of PyTorch or JAX and libraries such as Megatron-LM, NeMo, or DeepSpeed.
• Experience with optimization techniques like FSDP/ZeRO and gradient checkpointing.
• Hands-on experience writing custom GPU kernels in CUDA or Triton.
• Full proficiency in English.

Culture & Benefits

• Opportunity to work in the CORE team within the Generative AI supergroup.
• Collaborative global environment shipping research that impacts millions of users.
• Focus on cutting-edge tools including smart editing and AI video.
• Open to candidates across all experience levels, including 2026/2027 graduates and interns.