This role combines practical knowledge of model architectures with strong compiler and systems engineering skills.
Responsibilities
Bring up new models: Understand model architectures, load and convert weights, implement supported execution paths, and establish correctness against reference implementations.
Lower models to hardware using MLIR: Develop and extend dialects, graph transformations, lowering passes, and hardware-specific mappings.
Support model operations: Enable attention, matrix multiplication, normalization, positional embeddings, and other operators through compiler and kernel changes.
Optimize execution: Improve operator fusion, tensor layouts, tiling, memory allocation, data movement, and parallel execution.
Optimize inference: Tune prefill and decode performance, KV cache management, batching, and quantization to improve latency, throughput, and memory efficiency.
Validate model quality: Investigate numerical differences and measure the accuracy impact of precision changes and compiler optimizations.
Diagnose bottlenecks: Use profiling, execution traces, and hardware counters to identify compute, memory, communication, and runtime limitations.
Work closely with hardware, compiler, kernel, and runtime teams to deliver reliable model support and repeatable performance benchmarks.
Required qualifications
Strong programming skills in C++ and Python.
Hands-on experience bringing up and debugging ML models in PyTorch or a comparable framework.
Practical experience with MLIR, including dialects, rewrite patterns, transformation passes, and lowering pipelines.
Understanding of compiler fundamentals, including intermediate representations, dataflow analysis, and code generation.
Understanding of transformer architectures, attention mechanisms, tensor operations, and numerical precision.
Experience profiling and optimizing workloads on GPUs or other AI accelerators.
Ability to debug correctness and performance issues across model code, compiler-generated code, kernels, and runtime execution.
Preferred qualifications
Experience with LLM inference, including GQA, sliding-window attention, MoE, KV caching, and speculative decoding.
Experience with FP16, BF16, FP8, or low-bit quantization and their accuracy and performance tradeoffs.
Experience developing accelerator kernels or hardware-specific compiler backends.
Familiarity with distributed execution, model parallelism, and accelerator memory hierarchies.
Contributions to MLIR, LLVM, inference frameworks, or related open-source projects.
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
1. Build a breakthrough AI platform beyond the constraints of the GPU.
2. Publish and open source their cutting-edge AI research.
3. Work on one of the fastest AI supercomputers in the world.
4. Enjoy job stability with startup vitality.
5. Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us!
Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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