ML Algorithm Mapping and Performance Engineer, Core ML

Cerebras; United States; Remote; USD 140,000 - 220,000; Full time

Job summary

Cerebras Systems builds the world's largest AI chip and leads in fast ML training and inference. We seek an engineer to map advanced algorithms to Cerebras architecture, benchmark against GPUs, and understand trade-offs as models scale.

You will develop performance models, run experiments, and help push frontier-grade efficiency across our systems. You will combine analytical modeling with hands-on prototyping, spanning kernel-level to end-to-end performance, while evaluating new techniques and

Qualifications

• Experience building analytical and empirical performance models.
• Experience in ML training/inference workloads and performance analysis.
• Ability to design and run benchmarks across large-scale models.

Responsibilities

• Build analytical and empirical performance models for state-of-the-art ML training and inference algorithms.
• Characterize asymptotic behavior and how trade-offs scale with model size, sequence length, batch size, parallelism, and hardware.
• Construct Pareto frontiers across model quality, latency, throughput, memory, communication, and compute cost.
• Develop prototype implementations and benchmarks for Cerebras WSE and relevant GPU/software baselines.
• Analyze system behavior to identify kernel, compiler, runtime, communication, and algorithmic bottlenecks.
• Evaluate emerging techniques in parallel token processing and related areas.

Skills

Performance modeling

Benchmarking

Prototyping

Kernel-level optimization

Tools

GPU baselines

Job description

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.

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.
• Construct Pareto frontiers across model quality, latency, throughput, memory footprint, communication, and compute cost.
• Develop prototype implementations and benchmarks for the Cerebras WSE and relevant GPU or software baselines.
• Analyze system behavior to identify kernel, compiler, runtime, communication, and algorithmic bottlenecks.
• Evaluate emerging techniques in areas such as parallel token