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 |
|---|---|---|
| Applied Machine Learning Research Scientist This role focuses on applying and scaling modern machine learning techniques, particularly LLM post-training (RLHF, GRPO), on Cerebras' wafer-scale AI chip. The scientist will build and maintain training pipelines, evaluation frameworks, and optimize ML workflows across pretraining, fine-tuning, and alignment stages, working with large datasets and contributing to shared ML infrastructure. | Post-trainData | 9 |
| Senior ML Systems Engineer Senior ML Systems Engineer to join the SOTA Training Platform team, responsible for bringing up state-of-the-art open-source and proprietary ML models on Cerebras CSX systems. This role involves working across the full stack, including model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning, with a focus on debugging and improving the bring-up process. |
| Post-trainServe |
| 9 |
| Applied AI/ML Scientist Applied AI Scientist role focused on developing and customizing large language and deep learning models for customer problems using Cerebras' wafer-scale engine. Responsibilities include customer use case discovery, architecting and executing end-to-end training recipes, fine-tuning models, building agentic system components, and providing technical customer leadership. Requires strong expertise in deep learning, large model training/fine-tuning, Python, PyTorch, and distributed training. | Post-trainAgent | 9 |