Together AI
Data AI · Open-source model infra
- HQ
- San Francisco, US
- Website
- together.ai
Currently tracking 22 active AI roles, down 17% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $121k–$300k (avg $227k).
Data AI · Open-source model infra
Together AI currently has 25 active AI-related job listings. The majority of these roles, 80%, are focused on serving infrastructure. Engineering is the dominant function with 21 listings, and the United States is the primary hiring country with 19 roles. Frequent tech tags include model serving, inference infrastructure, and fine-tuning. Over the last 30 days, Together AI posted 4 new AI roles, representing a 33% decrease compared to the previous 30-day period.
Together AI currently has 26 active AI-related roles in our index. The most common open titles are: Solutions Architect (2), AI Infrastructure Engineer, AI Researcher, Core ML (Turbo), AI infrastructure Engineer (SRE) Bangalore , Customer Support Engineer (Inference), India. Most positions are in Engineering and Research.
Together AI's active AI hiring is concentrated in: serving infrastructure (81%), post-training (12%), application (4%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Together AI is hiring AI talent in: United States (20 roles), Netherlands (3 roles), United Kingdom (1 role).
Job postings at Together AI most frequently mention: Production ML Systems, Inference Infrastructure, GPU Computing, LLM Inference, Text-to-Speech.
In the past 30 days, Together AI has posted 5 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, Large-Scale Training Research Engineer focused on scaling and optimizing large-scale training infrastructure for foundation models, integrating new architectures, and productionizing novel training methods. | Post-trainData | 9 |
| Research Engineer, Post-Training Inference Research Engineer focused on customizing open-source foundation models for downstream applications. The role involves building and improving services for fine-tuning, reinforcement learning, and evaluation, with a strong emphasis on integrating post-training processes with production serving and optimizing inference for RL training workloads. Requires experience in building and deploying ML services, modern inference engines, and fine-tuning techniques. | Post-trainServe | 9 |
| Research Intern, Model Shaping (Fall 2026) Research intern role focused on advanced post-training methods (supervised learning, preference optimization, RL), efficient neural network training techniques, and robust evaluation of foundation models. The team tailors open foundation models for downstream applications and develops new methods for efficient training and evaluation. | Post-trainPretrain | 9 |