Senior Machine Learning Engineer, Search & Index

In this role you will design and operate the search and indexing platform for Wayve’s multimodal driving data, enabling scalable retrieval at billions of vectors. You’ll work with cross-functional teams to shape architecture, decide build vs. buy, and ship an MVP quickly. As the team’s first member in Sunnyvale, you’ll help expand capabilities and partner on data curation and research workflows. You’ll join a fast-paced, growth-oriented environment that values hands-on delivery and impact on autonomous driving at scale.

• salary benchmarked against market annually
• meaningful equity
• relocation support and visa sponsorship
• hybrid working model
• learning and development budgets
• comprehensive health and wellbeing benefits

• Design, build and operate the search and indexing platform for large-scale video and sensor data
• Own components from design through production with decisions on build vs. buy
• Develop ingestion and indexing pipelines for video, sensor data, embeddings and metadata
• Create backend services and APIs for vector similarity, semantic retrieval and metadata-filtered search
• Evaluate and integrate embedding models, vector databases and external vendors
• Contribute to evaluation frameworks measuring retrieval quality, latency, scalability and cost
• Support data curation and research workflows; mentor other engineers through code reviews

• 7+ years in backend, infrastructure, or ML systems delivering production-grade systems
• Hands-on experience with vector databases or ANN technologies (e.g., FAISS, LanceDB)
• Understanding of multimodal embeddings and evaluation of search quality across relevance and recall
• Proficient in Python or another systems language
• Experience building search services, APIs, and large-scale ingestion/indexing pipelines
• Track record delivering systems in ambiguous problem spaces
• Comfort working across infra, ML and data curation

• cross-functional collaboration
• mentoring and code reviews
• fast execution with MVP mindset
• vector databases/ANN technologies (FAISS, LanceDB)
• multimodal embeddings
• backend services and APIs