Staff Machine Learning Engineer

Role overview Lead the engineering of machine learning systems from data preparation and model training through evaluation, inference, and production operations. The role combines hands-on work on large-model pipelines and GPU-based infrastructure with architecture decisions and technical guidance for other engineers. The systems support AI features in a consumer-facing application.

Responsibilities - Design and build ML systems spanning data processing, training, evaluation, inference, and deployment. - Develop training and fine-tuning pipelines for large language models. - Create evaluation frameworks to assess model quality, reliability, and performance. - Improve inference latency, throughput, GPU utilisation, and cost. - Build pipelines to collect, process, and validate training and evaluation data. - Deploy, monitor, and troubleshoot models and infrastructure in production. - Make architecture decisions, review designs and code, and work with research and application teams to improve production model performance.

Requirements - At least 8 years of software or machine learning 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, inference latency, and resource use. - Experience designing ML architecture, resolving complex production issues, and providing technical leadership to ML engineers.

Nice to have - Experience with large language models, transformers, or distributed model training. - Familiarity with inference frameworks such as vLLM or TensorRT-LLM. - Experience with synthetic data, data curation, model evaluation pipelines, or consumer ML deployments.