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 |
|---|---|---|
| ML Research Engineer (Inference) Research Engineer focused on adapting and optimizing advanced language and vision models for efficient inference on Cerebras' wafer-scale AI architecture. The role involves implementing, validating, and optimizing models for low-latency, high-throughput inference, with a focus on techniques like speculative decoding, pruning, compression, and sparsity. | Serve | 9 |
| Advanced Technology: R&D Engineer - AI/ML, HPC Research Engineer role focused on designing and implementing AI/ML workloads on Cerebras' wafer-scale hardware, optimizing performance, and contributing to future hardware/software roadmaps. Involves algorithm-hardware co-design, performance modeling, and publishing research. | Serve | 9 |