Senior ML Engineer (Agentic Search)

We are seeking a Senior ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. Our focus is agentic search, where AI systems plan, retrieve and refine rather than just return results.

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, operating at the scale of web search.

Your responsibilities:

• Train and deploy ML models for retrieval, reranking, and search relevance, such as embedding models and cross-encoders, in production

• Develop models supporting crawling, data selection, and content understanding

• Build and maintain the data, training and evaluation pipelines behind our models: training-data generation, evaluation sets and retraining

• Contribute to and improve our ML platform: training and orchestration workflows (Kubernetes, GPU scheduling, experiment tracking, model lifecycle) and inference optimisation for low-latency serving

• Work closely with engineering to integrate and deploy ML models in production services, balancing latency, quality and cost

• Apply the latest architectural advances and training procedures for retrieval and LLM-integrated systems

Must-haves:

• 5+ years of experience in software engineering or applied machine learning

• Strong programming skills in Python and at least one of Go or C++

• Proven experience deploying ML models in production systems

• Solid understanding of machine learning and modern deep learning, including transformers and embeddings

• Experience building data or ML pipelines and working with workflow orchestration

• Ability to define meaningful model metrics and evaluate models rigorously

• Product-oriented mindset with a focus on impact and iteration

Nice-to-haves:

• Hands-on experience with retrieval, ranking, or recommendation

• Experience with Kubernetes, distributed training or GPU scheduling, or inference optimisation (batching, quantisation, compilation)

• Contributions to open-source projects, technical publications, or conference talks

• Strong results on public benchmarks or leaderboards (e.g. MTEB, BEIR) or models released on Hugging Face