Requirements:

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

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