ML Algorithm Mapping and Performance Engineer, Core ML
Sunnyvale, CA • FullTime
Posted 19d ago
About the job
The Core ML team is seeking an engineer to map novel algorithms for efficient large-scale AI training and inference to the Cerebras architecture. This role involves determining when these algorithms outperform competing approaches and how their advantages scale with models, workloads, and hardware. 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, influencing research directions, implementation on current systems, and future hardware/software capabilities.
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.
Requirements
- 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.
Benefits
- Build a breakthrough AI platform beyond the constraints of the GPU.
- 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.
- Simple, non-corporate work culture that respects individual beliefs.