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ML Algorithm Mapping and Performance Engineer, Core ML

Cerebras· Software Engineering
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Hybrid (Sunnyvale, CA)Full-timePosted 3w ago

About the role

Role focusing on mapping novel ML algorithms to Cerebras architecture, benchmarking, and co-design to optimize training/inference performance at scale.

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 generation, diffusion and speculative decoding, attention, sparsity, mixture-of-experts, low-precision computation, and distributed training.
  • Partner with researchers and kernel, compiler, runtime, inference, and architecture teams to recommend high-value implementation and co-design directions.
  • Develop tools and visualizations that make performance projections, measurements, and design trade-offs understandable across engineering and research teams.
  • Clearly communicate conclusions, assumptions, limitations, and recommendations through technical reports, presentations, and design reviews.

Qualifications

  • Bachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field.
  • Strong foundation in computer architecture, parallel computing, and systems performance.
  • Strong understanding of machine learning fundamentals and ML systems, including how model and algorithmic choices affect compute, memory, communication, accuracy, and scaling behavior.
  • Experience with analytical performance modeling, algorithmic complexity analysis, benchmarking, or system simulation.
  • Strong analytical and problem-solving skills, including the ability to reason from first principles about compute, memory, and communication costs.
  • Proficiency in Python and comfort with C++.
  • Experience profiling and debugging performance in an ML, HPC, CPU, GPU, or accelerator-based system.
  • Ability to move between mathematical analysis, experimental validation, and practical engineering recommendations.

Skills

Required:Performance modelingAlgorithmic analysisProgrammingProfiling & debuggingCommunication

Benefits

  • Not specified in the description.
Last updated Oct 4, 2026. Interested? Apply on the company site.

About Cerebras

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras is the go-to platform for fast and effortless AI training.

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