Senior Applied ML Engineer (Search)

• →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

• 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

• 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.