GPU cloud for AI inference, fine-tuning and model training
Together AI runs a GPU cloud for AI engineers, with a stack built on CUDA, Triton, FlashAttention, vLLM, SGLang and TensorRT-LLM across AWS, GCP and Azure. Adoption of WEKA, Lustre, Ceph and NVMe-oF points to a storage overhaul for large clusters, while Cartesia and Deepgram signal a push into real-time voice inference. Hiring is accelerating and skews heavily toward senior engineering, focused on GPU fleet reliability and kernel-level performance.
Together AI is a San Francisco-based, privately held company founded in 2022 with 201–500 employees. It offers a full-stack AI platform covering inference, fine-tuning and post-training, and pre-training on GPU infrastructure. Customers range from AI-native startups to established SaaS companies building AI applications. The platform spans a high-performance inference engine, on-demand GPU clusters and large-scale training capacity. The company is hiring across the United States, United Kingdom, Netherlands, Singapore and India.
Together AI uses PyTorch, CUDA, Triton, vLLM, SGLang, TensorRT-LLM and FlashAttention, running on Kubernetes and Slurm across AWS, GCP and Azure, with Ceph, HDFS and RDMA for storage and networking.
Yes. Engineering dominates Together AI's accelerating hiring, weighted toward senior and staff roles, alongside research, sales and operations positions.
Together AI hires in the United States, United Kingdom, Netherlands, Singapore and India, with headquarters in San Francisco, California.
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Together AI's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.