Together AI currently has 22 active AI-related job listings. The majority of these roles, 95%, are focused on serving infrastructure, with one role in post-training. The company is primarily hiring for Engineering positions, with 20 roles available, and is seeking candidates in the United States and the Netherlands. Frequent technical tags include model serving, inference infrastructure, and fine-tuning, suggesting a focus on deployment and optimization of AI models. In the last 30 days, Together AI added 5 new AI roles, a 150% increase from the previous 30-day period.
Currently tracking 20 active AI roles, with 14 new openings in the last 4 weeks. Primary focus: Serve · Engineering. Salary range $160k–$300k (avg $226k).
Together AI currently has 24 active AI-related roles in our index. The most common open titles are: Solutions Architect (2), AI Infrastructure Engineer, AI Researcher, Core ML (Turbo), Backend Software Engineer — Data Platform & AI Data Products, Customer Support Engineer (Inference). Most positions are in Engineering and Research.
Together AI's active AI hiring is concentrated in: serving infrastructure (96%), post-training (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 (19 roles), Netherlands (2 roles), United Kingdom (1 role).
Job postings at Together AI most frequently reference: inference infra, model serving, fine tuning, llm observability, audio speech.
In the past 30 days, Together AI has posted 6 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, Core ML Research Engineer role focused on improving inference efficiency and unifying it with RL/post-training systems for production-grade AI APIs. The role involves end-to-end ownership of critical systems, translating frontier ideas into robust infrastructure, and shipping measurable improvements in latency, throughput, cost, and model quality at scale. | ServePost-train | 10 |
| Research Engineer, Frontier Speculative Decoding Research Engineer focused on translating internal model training research into production-ready deployments by fine-tuning general-purpose models into specialized tools. This involves designing novel speculative algorithms, data curation, hyperparameter tuning, and checkpoint evaluation, with a focus on accuracy-efficiency tradeoffs for generative AI models. | Post-trainServe | 9 |