Are these based on real engineering roles?
Yes. The catalog is built around real engineering role patterns so the practice round feels closer to a live interview.
Company practice
Cerebras mock interviews
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
4 rolesPythonC++CI/CDCUDA
Create custom interviewRoles to practice
Cerebras
ML / AI
AI Engineer, Model Quality and Performance
Owns quality and performance for Cerebras' inference offerings by designing automated eval suites, mining customer workload data to build representative test datasets, and forecasting how those workloads will run on wafer-scale hardware. Builds agent-in-the-loop pipelines and dashboards that consolidate quality and performance metrics across model releases. A technical interview would probe eval design for LLMs (coding, agentic, multimodal), statistical reasoning about benchmark variance, and how you architect a self-running evaluation pipeline.
PythonDockerlm-eval-harnessCerebras
Research
Applied Machine Learning Research Scientist
Adapts and optimizes large language and vision models for efficient training and inference on Cerebras systems, researching architectures, sparsity, and numerical techniques that exploit wafer-scale compute. Publishes and partners with engineering to bring research into the production model stack. A technical interview would probe transformer internals, training-at-scale tradeoffs (parallelism, memory, precision), and the math behind an optimization or sparsity method you would propose.
PyTorchLLMsDistributed TrainingCerebras
Infrastructure
Compiler Engineer, Advanced Technology Group
Designs and develops the Tungsten language compiler that maps deep-learning workloads onto the Cerebras Wafer-Scale Engine, owning IR design, optimization passes, and code generation for a massively parallel custom-silicon target. Collaborates with hardware and kernel teams to extract performance from the WSE's on-wafer memory and fabric. A technical interview would probe compiler internals (dataflow analysis, loop/tiling transformations, IR lowering through MLIR/LLVM) and reasoning about scheduling computation across a non-von-Neumann architecture.
C++MLIRLLVMCerebras
Security
Security SWE
Builds and hardens the security posture of Cerebras' cloud and on-prem inference infrastructure, automating detection, secrets management, and access controls across Linux fleets and containerized services. Works across infra and ML platform teams to secure the path from customer data to wafer-scale compute. A technical interview would probe threat modeling of a multi-tenant ML service, Linux/container security internals, and how you would design least-privilege access and detection for a high-throughput inference cluster.
PythonLinuxKubernetes
ExoForm is not affiliated with Cerebras. This is an independent practice page.
FAQ
Is the interview voice-based?
Yes. ExoForm runs a live voice interview, asks follow-ups, and produces structured feedback after the session.
Can I try it for free?
Yes. You can start with the free interview allowance before upgrading for more practice.