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Forward Deployed Engineer (Inference & Post-Training) - Mandarin Speaking

Together AI· Customer Success
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Hybrid (Singapore)Posted 2mo ago

About the role

Hands-on technical partner for strategic customers focusing on inference optimization, fine-tuning pipelines, and production deployment of AI models. Collaborates with Solutions Architects to meet complex POCs, drive platform adoption, and optimize post-training workflows.

Responsibilities

  • Inference Engine Optimization: Select, configure, and optimize inference engine based on hardware, model architecture, and workload profile
  • Configuration & Performance Tuning: Develop configuration updates to win critical POCs, benchmarks, and optimize customer deployments; tune KV cache, apply speculative decoding, determine optimal tensor parallelism, and determine quantization strategy to hit throughput and latency targets.
  • Post-Training & Fine-Tuning: Drive hands-on RL training runs and optimize system design; guide customers through LoRA, SFT, DPO, RLHF, and GRPO pipelines from experimentation through production.
  • Strategic Customer Alignment: Act as the primary technical point of contact for aligned strategic accounts — monitoring and optimizing endpoint configurations, helping customers get the most out of the platform, and collaborating to ensure we hit critical milestones.
  • Opinionated Onboarding: Establish direct alignment with strategic customers at onboarding; ensure the right inference and post-training configurations are in place from day one to improve time-to-value.
  • Product Feedback Loop: Directly influence software and model roadmap by surfacing insights from the field; contribute back to product and drive early feature and research adoption with strategic logos.

Qualifications

  • 5+ years in a technical role, with a strong focus on inference systems, open-source LLM deployment, or post-training workflows.
  • Expert-level, hands-on experience with inference engines (e.g., vLLM, TensorRT-LLM, SGLang); ability to diagnose and resolve performance issues at the engine level.
  • Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization techniques.
  • Hands-on experience with fine-tuning and post-training pipelines, including LoRA, SFT, DPO, RLHF, and GRPO; ability to advise on system design.
  • Broad knowledge of state-of-the-art open-source models and judgment on model selection for specific customer use cases, hardware profiles, and performance targets.
  • Strong Python skills; comfortable working in production environments.

Skills

Required:Inference optimizationInference enginesKV cache tuningQuantizationPost-training pipelinesPython

Benefits

  • competitive compensation
  • startup equity
  • health insurance
  • remote work flexibility
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Together AI

The AI Acceleration Cloud - Fast Inference, Fine-Tuning & Training

Run and fine-tune generative AI models with simple APIs and scalable GPU clusters. Train & deploy at scale on The AI Acceleration Cloud.

AI native cloudFine-TuningGPU clustersInferenceTrainingai-infrastructure-and-developmentbrex-fastest-growing-2025liquid-ai-recruiting-target
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