Cerebras currently has 38 active AI-related job listings. The majority of these roles, 79%, are focused on serving infrastructure. The top hiring function is Engineering, with 32 roles. The company is actively hiring in the United States and Canada. Frequent tech tags include model_serving and inference_infra. In the last 30 days, Cerebras posted 4 new AI roles, representing a 20% decrease compared to the previous 30-day period.
Currently tracking 36 active AI roles, up 46% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $170k–$250k (avg $206k).
Cerebras currently has 39 active AI-related roles in our index. The most common open titles are: Kernel Engineer (2), ML Systems Performance Engineer (2), LLM Inference Performance & Evals Engineer, AI Infrastructure Operations Engineer, AI Models, Product Manager. Most positions are in Engineering and Research.
Cerebras's active AI hiring is concentrated in: serving infrastructure (85%), post-training (8%), pre-training (5%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Cerebras is hiring AI talent in: United States (23 roles), Canada (20 roles), India (6 roles), United Arab Emirates (3 roles).
Job postings at Cerebras most frequently reference: model serving, inference infra, fine tuning, llm observability, frontier research.
In the past 30 days, Cerebras has posted 4 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Advanced Technology: AI/ML Research Scientist Research Scientist role focused on designing AI models and training methods from first principles, leveraging novel wafer-scale hardware architectures. The role involves investigating computational science techniques for AI, understanding hardware-algorithm interactions, and publishing research at top-tier venues. The work directly influences future hardware and software design. | Pretrain | 10 |
| Principal ML Investigator Cerebras is seeking a Principal ML Investigator to lead a new ML team focused on advanced development in areas like post-training, reinforcement learning, dataset curation, LLM pretraining, sparsity, and various domains (coding agents, reasoning agents, generative language, image, video). The role involves building the team, formulating research agendas, adapting algorithms to Cerebras hardware, training/tuning/evaluating models, and collaborating with internal and external partners. |
| PretrainPost-train |
| 9 |