Research Engineer, Post-Training Inference
Together AI · San Francisco
CHECKED 1H AGO
Infrastructureonsite$200k–$290k
At a glance
Highlights
- Build platform enabling users to customize open-source models with own data
- Ensure seamless path from post-training fine-tuning to production serving
- Optimize inference engines for large-scale RL training workloads
What they offer
- startup equity
- health insurance
Ideal candidate
A production-minded ML engineer who loves bridging training and inference to empower open-source builders with reliable, high-performance customization.
Requirements
- 2+ years building and deploying ML-based services in production
- Hands-on experience with modern inference engines like SGLang, vLLM, TensorRT-LLM
- Familiar with latest methods for fine-tuning LLMs and other AI models
- Strong software engineering background in Python or Go
- Stays up to date with latest advances in ML community
Lab profile
About Together AI
AI-native cloud for training and inference on large-scale GPU infrastructure for AI apps and agents.
What they build
- AI-native cloud platform
- GPU clusters for training and inference
- Platform for AI applications and agents
Known for
- Operating large-scale GPU fleets
- Automated infrastructure at massive scale
- Multi-site data center deployment
Key facts
- Over 1M developers
- Operates one of the world's largest GPU fleets
- Infrastructure managing tens of thousands of GPUs