About the job
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 data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad range of ideas across machine learning, natural language processing, and ML systems.
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
Qualifications
Minimum
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
Preferred
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