Full Stack Machine Learning Engineer

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.

What you'll do

• 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
• Work closely with hardware, compiler, kernel, and runtime teams to deliver reliable model support and repeatable performance benchmarks

What you need

• Machine learning
• Python
• PyTorch
• C++

Good to have

• LLMs