Cerebras Systems
ML Algorithm Mapping and Performance Engineer, Core ML
Sunnyvale, CA
Stay score
odds of building a lasting career here
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- 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.
- Develop prototype implementations and benchmarks for the Cerebras WSE and relevant GPU or software baselines.
- 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.
- People who are serious about software make their own hardware.
- Build a breakthrough AI platform beyond the constraints of the GPU.
Requirements
- Bachelor's, Master's, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field.
- Experience with analytical performance modeling, algorithmic complexity analysis, benchmarking, or system simulation.
- Proficiency in Python and comfort with C++.
- Experience with roofline analysis, CPU or GPU simulators, kernel optimization, or hardware-software co-design.
Nice to have
- Preferred Skills & Qualifications
Skills
- Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups.
Benefits
- Strong understanding of machine learning fundamentals and ML systems, including how model and algorithmic choices affect compute, memory, communication, accuracy, and scaling behavior.
Company info
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
- At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry.
This listing is sourced directly from Cerebras Systems's careers page and normalized into a canonical job model.