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 |
|---|---|---|
| Lead Full Stack Machine Learning Engineer This role focuses on bringing up and optimizing open-source AI models and frameworks on Cerebras' wafer-scale hardware. It involves working across the full software stack, from model translation and compiler optimizations to runtime integration and performance tuning, with a strong emphasis on debugging and improving the bring-up process for future models. | ServePost-train | 9 |
| 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. |
| 9 |
| Kernel Engineer Kernel Engineer role focused on developing and optimizing high-performance software for Cerebras' AI chip, specifically implementing and scaling deep learning operations and building parallel algorithms for training and inference. The role involves low-level programming, performance tuning, and interaction with hardware architects to maximize compute utilization and accelerate AI innovation. | ServePretrain | 9 |
| ML Systems Performance Engineer ML Systems Performance Engineer role focused on optimizing inference speed and throughput on Cerebras' custom wafer-scale AI chip. Responsibilities include building performance models, optimizing kernel microcode and compiler algorithms, debugging runtime performance, and developing performance visualization tools. Requires strong background in computer architecture, low-level deep learning math, and experience with performance profiling and optimization on CPU/GPU simulators. | Serve | 8 |
| QA Lead (ML Integration and Quality) The QA Lead will be responsible for ensuring the quality of Cerebras' software across all supported ML workloads and workflows, focusing on feature testing, ML training accuracy and performance, and pre-deployment validation. This role involves driving quality, implementing testing methodologies, automating workflows, and debugging issues within a large-scale enterprise environment. | ServePost-train | 7 |