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Together AITogether AISan Francisco, CA

Research Engineer, Post-Training Inference

Research Engineer building platforms to customize open-source LLMs via fine-tuning, RL, and evaluation. Focus on integrating post-training with inference engines (vLLM, SGLang, TensorRT-LLM), optimizing for RL workloads, and ensuring production reliability. Requires 2+ years ML production experience and strong Python/Go skills.

200k – 290k
On-site2+ YOEML Engineering

About the role

Responsibilities

  • Design and build Together’s systems for customizing open-source models
  • Build integrations between the Model Shaping and Inference platforms to ensure a seamless path from post-training to serving production workloads
  • Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads
  • Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform

Requirements

  • 2+ years of experience building and deploying machine learning-based services in a production environment
  • Hands-on experience with modern inference engines, such as SGLang, vLLM, and TensorRT-LLM
  • Familiar with the latest methods for fine-tuning LLMs and other AI models
  • Strong software engineering background in Python or Go
  • Stay up to date with the latest advances and trends in the machine learning community

Nice-to-Haves

  • Serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes
  • Optimizing the performance of RL training workloads
  • Developing CUDA/Triton/CuTE DSL kernels for inference
  • Developing large-scale and high-load production systems
  • Maintaining or contributing to open-source ML projects
  • Managing machine learning workloads on Kubernetes clusters

Compensation

US base salary range for this full-time position is $200,000 - $290,000.

Skills

SglangvLLMTensorrt-LlmPythonGoCUDATritonKubernetesLoraRLHFLlm Fine-Tuning

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