ML Algorithm Mapping and Performance Engineer

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 with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

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 with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About The Role

The Core ML team develops novel algorithms for efficient large-scale training and inference. We are looking for an engineer who can determine how these algorithms should be mapped to the Cerebras architecture, when they outperform competing approaches, and how their advantages change as models, workloads, and hardware systems scale.

You will combine analytical performance modeling, empirical benchmarking, and hands-on prototyping to characterize the efficiency frontiers of emerging ML algorithms. Your work will span kernel-level and end-to-end performance, helping the team reason about trade-offs among model quality, latency, throughput, memory, communication, and compute utilization.

This role will directly influence which research ideas Core ML pursues, how those ideas are implemented on current Cerebras systems, and which capabilities should be considered in future generations of hardware and software.

Responsibilities

• Build analytical and empirical performance models for state-of-the-art ML training and inference algorithms.

• Characterize asymptotic behavior and identify how algorithmic trade-offs change with model size, sequence length, batch size, parallelism, and hardware scale.