Senior Research Engineer

Strong expertise in the Python ecosystem and major ML frameworks (PyTorch, JAX). Experience with lower-level programming (C++ or Rust preferred). Deep understanding of GPU acceleration (CUDA, profiling, kernel-level optimization); TPU experience is a strong plus. Proven ability to accelerate deep learning workloads using compiler frameworks, graph optimizations, and parallelization strategies. Solid understanding of the deep learning lifecycle: model design, large-scale training, data processing pipelines, and inference deployment. Strong debugging, profiling, and optimization skills in large-scale distributed environments. Excellent communication and collaboration skills, with the ability to clearly prioritize and articulate impact-driven technical solutions.

Investigate and mitigate performance bottlenecks in large-scale distributed training and inference systems. Develop and implement both low-level (operator/kernel) and high-level (system/architecture) optimization strategies. Translate research models and prototypes into highly optimized, production-ready inference systems. Explore and integrate inference compilers such as TensorRT, ONNX Runtime, AWS Neuron and Inferentia, or similar technologies. Design, test, and deploy scalable solutions for parallel and distributed workloads on heterogeneous hardware. Facilitate knowledge transfer and bidirectional support between Research and Engineering teams, ensuring alignment of priorities and solutions.