Research MLE (Training Optimization）大模型训练优化工程师

What you’ll do (responsibilities)

• You’ll design, implement, and optimize large-scale machine learning systems for training
• You’ll improve all aspects of performance, including GPU utilization, communication overhead, and memory efficiency.
• You’ll partner with research and modeling teams to align systems with algorithmic needs.
• You’ll evaluate and apply best practices for distributed training using industry-leading frameworks.
• You’ll dive deep into low-level optimization, including custom CUDA or Triton kernels.

• • You’ll debug, profile, and fine-tune training workflows to unlock new levels of scalability.

What we're looking for

We’re looking for a systems-first engineer who thrives in fast-paced, high-impact environments. You’re deeply familiar with distributed model training at scale and understand the nuances of optimizing compute at every level of the stack. You're excited by challenges that stretch current boundaries, and you’re a strong collaborator who communicates clearly across domains.

• Strong background in LLMs, multimodal AI, or diffusion models.
• Proficiency in Python. Familiarity with a system programming language (e.g. C++ or Rust) is a plus.
• Deep knowledge of PyTorch or JAX as well as libraries such as Megatron-LM, NeMo, or DeepSpeed.
• Familiarity with common optimization techniques such as FSDP/ZeRO, gradient checkpointing, or low-precision data types.
• Hands-on experience writing custom GPU kernels in CUDA or Triton.
• Excellent communication and problem-solving skills, incl. full proficiency in English.

大模型训练优化工程师（多模态/图像生成），技术要求：算子优化/分布式训练/GPU集群/训练框架。该岗位面向所有经验阶段的候选人开放，包括社会招聘、应届毕业生，同时开放实习生岗位。