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Full Stack Machine Learning Engineer

Cerebras· Software Engineering
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Hybrid (Bengaluru, IND)Full-timePosted 2w ago

About the role

Role focused on taking ML models from reference implementations to efficient execution on Cerebras AI accelerator hardware, including MLIR-based lowering, validation, and performance optimization across model, compiler, kernels, and runtime.

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.

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.

Skills

Required:C++PythonPyTorchMLIRCompiler fundamentals
Last updated Oct 18, 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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