Applied ML Engineer

Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems. You will develop and deploy ML models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production; Build and optimise embedding-based indexing and large-scale retrieval systems; Develop models supporting crawling, data selection, and content understanding; Define and improve quality metrics for agent-native search and build evaluation pipelines; Work on systems operating at very large scale, including high-throughput query workloads; Collaborate closely with engineering teams to integrate ML models into production services; Analyse performance trade-offs across latency, quality, and cost; Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems; Contribute to product and architectural decisions in a fast-moving environment. Must-haves: 5+ years of experience in software engineering or applied machine learning; Strong programming skills in Python, Go, or C++; Proven experience deploying ML models in production systems; Hands-on experience with retrieval, ranking, recommendation, or similar ML problems; Strong understanding of machine learning and modern deep learning techniques; Experience working with large-scale data systems and high-throughput environments; Ability to design evaluation frameworks and define meaningful model metrics; Product-oriented mindset with a focus on impact and iteration; Strong problem-solving skills and ability to work in a distributed team. Nice-to-haves: Experience with search systems or large-scale information retrieval; Familiarity with embeddings, transformers, and modern NLP systems; Experience working on LLM-powered or agent-based systems; Contributions to open-source projects, technical publications, or conference talks; Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability; We conduct coding interviews as part of the process.