Full Stack Machine Learning Engineer

About the role

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the role

We are looking for an ML Engineer who can take a model from its reference implementation to efficient execution on AI accelerator hardware. You will own model bring-up, develop MLIR-based lowering paths, validate numerical correctness, and optimize performance across the model, compiler, kernels, and runtime.

This role combines practical knowledge of model architectures with strong compiler and systems engineering skills.

We are looking for an ML Engineer who can take a model from its reference implementation to efficient execution on AI accelerator hardware. You will own model bring-up, develop MLIR-based lowering paths, validate numerical correctness, and optimize performance across the model, compiler, kernels, and runtime.

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.