Senior Applied ML Engineer (Agentic Search)

We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning 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.