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.
About the job
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
Sglang, vLLM, Tensorrt-Llm, Python, Go, CUDA, Triton, Kubernetes, Lora, RLHF, Llm Fine-Tuning
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