Research Engineer, Post-Training Inference

About this role

Join Together AI's Model Shaping team to build systems that enable developers to customize open-source foundation models with their own data. You'll work across the training and inference stacks, optimizing fine-tuning, reinforcement learning, and evaluation services while ensuring seamless deployment from post-training to production.

What you'll do

• Design and build systems for customizing open-source models at scale
• Integrate Model Shaping and Inference platforms for end-to-end post-training workflows
• Develop inference engine features and optimizations for RL training workloads
• Ensure platform stability, reliability, and 24/7 availability through on-call rotation
• Collaborate with product, research, and engineering teams on API design and integration
• Optimize inference performance for low-precision models and distributed GPU setups

What they're looking for

• Python or Go software engineering
• Inference engines (vLLM, SGLang, TensorRT-LLM)
• LLM fine-tuning methods and techniques
• Production ML systems design and deployment
• CUDA/Triton kernel development (preferred)
• Kubernetes for ML workload management (preferred)
• RL training optimization
• Multi-LoRA and distributed model serving

Benefits

• Competitive salary ($200,000–$290,000 base)
• Startup equity
• Health insurance
• Work on foundational open-source AI infrastructure
• Collaborate with leading ML researchers
• San Francisco location

Likely interview questions

• Walk us through your experience deploying production ML services—what were the biggest challenges you faced around reliability and performance?
• Which inference engines have you worked with, and how did you optimize them for your specific use cases?