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Forward Deployment Engineer -Chinese speaking

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Job Description - Forward Deployment Engineer -Chinese speaking

About the Role

We are looking for a Forward Deployment Engineer (FDE) to work directly with customers to design, deploy, and validate inference & reinforcement learning POCs on GMI's GPU infrastructure. This is a hybrid role spanning platform engineering, applied ML, and customer success – you'll turn research ideas into performant systems on real GPU clusters.

What You'll Do

  • Own customer POCs end-to-end: deploy and optimize LLM inference, RL training, and post-training workflows
  • Work hands-on with research teams, startups, and enterprise customers
  • Debug performance, stability, and correctness in real environments
  • Stand up and tune inference stacks (vLLM, SGLang, Ray Serve, etc.)
  • Optimize latency, throughput, GPU utilization, and cost efficiency
  • Support RLHF / RFT / SFT workflows using customer datasets
  • Diagnose GPU, networking, and distributed system bottlenecks
  • Feed customer learnings back into platform, SDKs, and APIs

What You Bring

  • Strong software engineering background (Python required; Go/Rust a plus)
  • Hands-on experience with ML inference or training systems
  • Familiarity with distributed systems and GPUs (multi-GPU, multi-node)
  • Experience with LLM inference frameworks (vLLM, SGLang, Ray Serve, Triton, etc.) – nice to have
  • Experience with RL or post-training workflows (RLHF, RFT, SFT) – nice to have
  • Knowledge of PyTorch, DeepSpeed, Megatron-LM, or Kubernetes-based ML platforms – nice to have
  • Comfort working directly with customers and ambiguous requirements
  • Ability to debug end-to-end systems (code, infra, networking, performance)
  • Bilingual in English and Mandarin (required to interface with engineering teams in China)
  • 2+ years of experience (no need for deep systems expertise, but strong execution mindset)
  • Fast learner, strong communication and presentation skills

Why Join Us

  • Work on cutting-edge inference and RL workloads – no toy demos
  • Close to real users and real GPUs, not abstract roadmaps
  • High ownership, fast iteration, and visible impact
  • Visa sponsorship (H1B) and green card support available based on performance
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