ai算法工程师（后训练）.

• Design, provision, and operate large-scale distributed training environments across multi-GPU, multi-node clusters using Kubernetes and managed ML platforms (AWS SageMaker HyperPod/EKS, Tencent TiOne, etc.).
• Configure and tune model/data parallelism strategies (data, tensor, pipeline, sequence, MoE) to maximize throughput.
• Improve training efficiency, manage storage pipelines (S3, EFS, FSx for Lustre), and engineer robust checkpointing/fault tolerance.
• Build observability tools, CI/CD pipelines, and infrastructure-as-code for training.
• Design and run post-training workflows (SFT, RFT/RLVR/RLAIF, continual pretraining) and integrate expert models into production drug-discovery workflows.

skills and experience required.

• Education: Bachelor in Computer Science and above.
• Experience Level: 5+ years of hands-on experience spanning ML infrastructure / LLMOps and LLM training.
• Technical Skills:
  • Deep, hands-on expertise with Kubernetes for large-model training across multiple platforms (AWS, GCP, Azure, or Tencent TiOne).
  • Strong understanding of distributed-training parallelism strategies.
  • Hands-on experience with frameworks like PyTorch (DDP/FSDP), DeepSpeed/ZeRO, and the Hugging Face ecosystem.
  • Fluency with cloud storage and high-throughput data systems (S3, EFS, FSx for Lustre).
  • Practical understanding of post-training and RL methods (SFT, GRPO, PPO, DPO, reward modeling).
  • Strong Python, container/orchestration (Docker/Kubernetes), and infrastructure-as-code skills.
• Desirable Extras (Nice-to-Have):
  • Experience with Megatron-LM, NVIDIA NeMo, TRL, verl, or vLLM.
  • Knowledge of cluster networking (NCCL tuning, EFA/RDMA/InfiniBand) and schedulers (Slurm, Ray, Volcano).
  • Familiarity with AI-for-Science applications (biomedical, chemical) and experiment tracking tools (W&B, MLflow).