Senior Machine Learning Engineer, Search & Index
Full-time
Wayve
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. ️ About Our Engineering Teams The Search & Indexing team, part of Compute and Data Platform (AI Platform), makes Wayve's multimodal driving data discoverable. We build the indexing infrastructure behind vector and metadata-based retrieval, powering data curation, training set construction and ML research. You'd be the first hire in the team, in our Sunnyvale office. Your day-to-day You'll design, build and operate the search and indexing platform for hundreds of thousands of hours of video and sensor data, with the system scaling beyond 10B vectors. You'll work with the Technical Lead on architecture, own complex components from design through production, and help decide what to build versus buy. Expect to move fast: use off-the-shelf tools and ship an MVP in months, not quarters. You'll work with ML, Data, Evaluation and Platform teams, and support other engineers through code reviews and mentoring. What You'll Be Working On
- Production search and indexing systems at billion-vector scale
- Ingestion and indexing pipelines across video, sensor data, embeddings and metadata
- 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
- Evaluation frameworks measuring retrieval quality, recall, relevance, filtering accuracy, latency, freshness, scalability and cost
- Integrating search into training-set construction, data curation and research workflows
- You have 7+ years in backend, infrastructure or ML systems, building production systems
- You have hands-on experience with vector databases or ANN technologies such as FAISS, LanceDB or similar
- You understand multimodal embeddings and have evaluated search quality across relevance, recall, filtering accuracy and performance
- You code strongly in Python or another systems language
- You have built search services, APIs, and large-scale ingestion and indexing pipelines
- You have delivered 0→1 systems in ambiguous problem spaces
- You are comfortable working across infra, ML and data curation
- Large-scale ML pipelines or distributed data infrastructure
- Multimodal data, including video, images, sensor data and metadata
- Active learning, semantic retrieval or training-data selection workflows
- Autonomy, robotics or large-scale data infrastructure
- Evaluating technical vendors and working with external providers
- Initial call / recruiter screen (30 mins)
- Deep-dive technical interviews [programming, system design & domain-specific interviews; 3 hours total]
- Final interview: mission & values alignment [45 mins].
Vacancy posted 2 days ago
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