AI Engineer, Model Quality and Performance
Cerebras · Headquarters/Sunnyvale Office
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. ABOUT THE ROLE You'll own model quality and performance for Cerebras' inference offerings. You will define what "good" looks like across the models we serve, building AI-driven systems to measure it at scale, and translating those signals into artifacts our customers and product team actually use. You'll use AI agents to spin up custom eval suites per customer use case, mine trajectories for representative test data, automate the repetitive parts of release qual, and help build performance datasets and benchmarking workflows for customer use cases. We want someone whose first instinct is "how do I get an AI agent to do this on a loop." You'll sit between engineering, product, and customer-facing teams. What You'll Do - Design eval suites with AI agents in the loop. For every model release, curate a thoughtful mix of advanced, basic, long-context, and customer-use-case-specific evals. Use Claude to generate, validate, and prune candidate test cases at speed. - Build custom evals for target customers by orchestrating AI agents to mine trajectories from their workloads and synthesize representative eval sets. - Automate eval execution end-to-end with AI-driven pipelines on top of standard tooling (Docker, Git, CI). The g