Machine Learning Engineer, Applied AI

Role overview Build and operate machine learning models that help a consumer communication product understand context and respond to people’s needs with minimal prompting. The work spans classification, search and retrieval, ranking, and personalisation, with a focus on making models useful and dependable in production.

Responsibilities - Develop and improve models for classification, retrieval, ranking, and personalisation. - Improve search relevance and context retrieval using embeddings, vector search, hybrid methods, or reranking. - Build datasets, evaluation pipelines, and metrics to measure quality and catch regressions. - Train, fine-tune, and deploy models through production workflows and inference services. - Monitor model performance, investigate production issues, and use evaluation results and user feedback to guide improvements. - Balance model quality with precision, latency, inference cost, and reliability.

Requirements - At least three years of machine learning engineering experience, including deploying models to production. - Strong Python skills and experience with PyTorch or JAX. - Sound knowledge of machine learning algorithms, training, and evaluation. - Experience with classification, ranking, retrieval, recommendation, or personalisation models. - Experience with embeddings, vector search, or information retrieval systems. - Ability to build data and training workflows, evaluate models with relevant metrics, and reason about accuracy, latency, cost, and reliability.

Nice to have - Experience with transformers, language model fine-tuning, or embedding models. - Familiarity with hybrid search, reranking, and retrieval evaluation. - Consumer product recommendation or personalisation experience. - Experience with large-scale model training or inference infrastructure.