Research Engineer, Post-Training Inference

Job summary

Responsibilities

The Model Shaping team at Together AI works on products and research focused on tailoring open foundation models to downstream applications. We build services that enable machine learning developers to choose the best models for their tasks and further improve these models using domain-specific…

• 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

What they're looking for

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

Tech stack

• vLLM
• Python
• CUDA
• Triton
• Kubernetes