Staff Machine Learning Engineer

Role overview Lead the engineering of machine learning systems from data preparation and model development through evaluation, inference, and production operations. The role combines hands-on work on large models and GPU infrastructure with technical direction for other ML engineers, partnering with research and application teams to improve real-world performance.

Responsibilities - Design and build systems for model data processing, training, evaluation, inference, and deployment. - Develop training and fine-tuning pipelines for large language models, along with evaluation methods for quality, reliability, and performance. - Build data pipelines to collect, process, and validate training and evaluation datasets. - Improve inference latency, throughput, GPU use, and operating cost. - Deploy, monitor, and troubleshoot production models and ML infrastructure. - Set technical direction, review designs and code, and work with research and application engineers to improve model performance.

Requirements - At least 8 years of software or machine learning engineering experience, including production ML systems. - Strong Python skills and practical experience with PyTorch or JAX. - Experience training, fine-tuning, and evaluating large machine learning models. - Experience building and operating GPU-based training or inference systems. - Strong understanding of distributed computing, data pipelines, and ML infrastructure. - Ability to optimize models and inference resources, resolve complex production issues, and provide technical leadership.

Nice to have - Experience with large language models, transformers, or distributed model training. - Familiarity with inference tools such as vLLM or TensorRT-LLM. - Experience with synthetic data, data curation, and model evaluation pipelines. - Background deploying ML systems for consumer applications.