Technical Lead, Machine Learning

Role overview Lead the engineering of production machine learning systems, from data preparation and training through evaluation, inference and deployment. You’ll shape system architecture, improve model performance in production, and provide technical direction to other ML engineers while partnering with research and application engineering teams.

Responsibilities - Design and build systems for ML data processing, model training, evaluation, inference and deployment. - Develop training and fine-tuning pipelines for large language models, along with frameworks to assess model quality and reliability. - Build data pipelines that collect, process and validate training and evaluation datasets. - Improve inference latency, throughput, GPU use and cost; operate and troubleshoot models and infrastructure in production. - Make architecture decisions, review designs and code, and resolve complex production issues. - Work with research and application teams to improve models in real-world use, while guiding ML engineers technically.

Requirements - At least eight years of software or ML engineering experience, including production ML systems. - Strong Python skills and hands-on 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 knowledge of distributed computing, data pipelines and ML infrastructure. - Ability to optimise model performance and resource use, design system architecture, 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 or model evaluation pipelines. - Experience deploying ML systems for consumer applications.