Senior Machine Learning Engineer Search and Index
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build production search and indexing systems for multimodal video and sensor data. You will evaluate embedding models and retrieval quality, develop search APIs, own components through production operations, integrate search into ML workflows, and guide other engineers through reviews and mentoring.
Requirements
- 7+ years of backend, infrastructure, or ML systems experience
- Experience with vector databases or ANN technologies such as FAISS or LanceDB
- Understanding of multimodal embeddings and search-quality evaluation
- Python or systems-language coding skills
- Experience building search services, APIs, and large-scale ingestion and indexing pipelines
- Experience delivering 0-to-1 systems
- Experience working across infrastructure, ML, and data-curation boundaries
- Experience with distributed data infrastructure, multimodal data, active learning, semantic retrieval, training-data selection, autonomy, robotics, or vendor evaluation is desirable
Responsibilities
- Build and operate billion-vector-scale production search and indexing systems
- Evaluate and integrate embedding models for semantic and multimodal retrieval
- Build frameworks to measure retrieval quality, recall, relevance, latency, freshness, scalability, and cost
- Develop backend services and APIs for vector similarity and metadata-filtered search
- Own search components from design through production operations
- Integrate search into training, curation, and evaluation workflows
- Provide code reviews, technical guidance, knowledge sharing, and mentoring
Benefits
- Competitive equity package
- Hybrid work arrangement