Research Engineer, Post-Training Inference

Together AI•San Francisco, CA

1d•$200,000 - $290,000•Remote

About The Position

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. As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data. Working across the training and inference stacks, you will build and improve our Fine-Tuning, Reinforcement Learning, and Evaluation services – from ensuring a seamless path from post-training to production serving, to optimizing the inference engine for RL training workloads. You will collaborate closely with our product, research, and engineering teams to keep the API reliable, performant, and well integrated into the company's technical infrastructure. Above all, you will help build the foundational layer of the open-source AI ecosystem, enabling developers around the world to efficiently create high-quality models tailored to their specific applications.

Requirements

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

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

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