SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. You would join the Search & Indexing team, part of the Compute and Data Platform (AI Platform), as the first hire in this newly formed team based in the Sunnyvale office.
Your role centers on designing, building, and operating a search and indexing platform for hundreds of thousands of hours of video and sensor data, scaling to over 10 billion vectors. You will work closely with the Technical Lead on architecture decisions, own complex components from design through production, and help determine what to build versus buy. The team emphasizes moving fast—shipping MVPs in months rather than quarters using off-the-shelf tools where appropriate.
Day-to-day responsibilities include:
- Designing and operating production search and indexing systems at billion-vector scale
- Building ingestion and indexing pipelines across video, sensor data, embeddings, and metadata
- Developing backend services and APIs for vector similarity, semantic retrieval, and metadata-filtered search
- Evaluating and integrating embedding models, vector databases, ANN libraries, and external vendors
- Creating evaluation frameworks to measure retrieval quality, recall, relevance, filtering accuracy, latency, freshness, scalability, and cost
- Integrating search capabilities into training-set construction, data curation, and research workflows
You will collaborate across ML, Data, Evaluation, and Platform teams, and support other engineers through code reviews and mentoring. Wayve operates a hybrid model combining in-person collaboration in dedicated office spaces with focused remote work time.
Requirements:
- 7+ years of professional experience in backend, infrastructure, or ML systems, with a track record of building production systems
- Hands-on experience with vector databases or ANN technologies (FAISS, LanceDB, or similar)
- Understanding of multimodal embeddings and demonstrated experience evaluating search quality across relevance, recall, filtering accuracy, and performance metrics
- Strong coding ability in Python or another systems language
- Experience building search services, APIs, and large-scale ingestion and indexing pipelines
- Track record of delivering 0→1 systems in ambiguous problem spaces
- Comfort working across infrastructure, ML, and data curation domains
Nice-to-have qualifications:
- Large-scale ML pipelines or distributed data infrastructure experience
- Exposure to multimodal data (video, images, sensor data, metadata)
- Background in active learning, semantic retrieval, or training-data selection workflows
- Experience in autonomy, robotics, or large-scale data infrastructure
- Experience evaluating technical vendors and working with external providers
