Cerebras
Semiconductors · Wafer-scale AI chip
- HQ
- Sunnyvale, US
- Founded
- 2016
- Website
- cerebras.net
Semiconductors · Wafer-scale AI chip
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 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 |
| Staff Kernel Optimzation Engineer Staff Kernel Optimization Engineer role focused on developing and optimizing high-performance software for Cerebras' custom wafer-scale AI chip, specifically for deep learning operations and inference. This involves implementing and debugging low-level kernels, mapping algorithms to hardware, and studying emerging AI trends to evolve kernel library architecture. The role contributes to accelerating AI innovation and delivering industry-leading training and inference speeds. |
| ServePretrain |
| 8 |
| Staff Python / PyTorch Developer — Frontend Inference Compiler – Dubai Staff Python/PyTorch Developer for Frontend Inference Compiler at Cerebras, focusing on optimizing generative AI models for their wafer-scale AI chip. Responsibilities include developing compiler infrastructure, analyzing new models, and improving inference performance. | Serve | 8 |