Forward Deployed Engineer (Inference & Post-Training) - Mandarin Speaking
Salary not listed • Singapore • Full-time
Posted 2mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to strategic customers looking to leverage high-quality models and perform inference at scale. You will act as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment, partnering with Solutions Architects. FDEs add significant value by ensuring complex Proofs of Concept (POCs) are met, facilitating platform adoption, and guiding tailored optimization efforts, directly impacting customer success and platform hardening.
Responsibilities
- Optimize inference engines based on hardware, model architecture, and workload.
- Develop configuration updates and tune performance for critical POCs and customer deployments, including KV cache, speculative decoding, tensor parallelism, and quantization.
- Drive hands-on RL training runs and optimize system design for LoRA, SFT, DPO, RLHF, and GRPO pipelines.
- Serve as the primary technical contact for strategic accounts, monitoring and optimizing endpoint configurations.
- Ensure optimal inference and post-training configurations are in place from day one for strategic customers.
- Influence the software and model roadmap by providing field insights and driving early feature adoption.
Requirements
- 5+ years in a technical role with a focus on inference systems, open-source LLM deployment, or post-training workflows.
- Expert-level, hands-on experience with inference engines like vLLM, TensorRT-LLM, or SGLang.
- Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization.
- Hands-on experience with fine-tuning and post-training pipelines (LoRA, SFT, DPO, RLHF, GRPO).
- Broad knowledge of state-of-the-art open-source models and strong model selection judgment.
- Strong Python skills and comfort working in production environments.
Benefits
- Competitive compensation
- Startup equity
- Health insurance
- Flexibility in terms of remote work