Senior Machine Learning Engineer, Wayve

• 7+ years of experience in backend, infrastructure, or ML systems, building production systems
• Experience with vector databases or ANN technologies such as FAISS, LanceDB, or similar open-source solutions
• Strong understanding of multimodal embeddings and experience evaluating search quality across relevance, recall, filtering accuracy, and performance
• Strong coding skills in Python or other systems languages
• Experience building search services, APIs, and large-scale ingestion and indexing pipelines
• Experience delivering 0→1 systems in ambiguous or exploratory problem spaces
• Comfortable working across system boundaries (infra, ML, data curation)

Desirable

• We are hiring a Senior Machine Learning Engineer for our Search & Indexing team
• The team is responsible for making Wayve’s massive corpus of multimodal video data discoverable and searchable
• This team builds the indexing infrastructure that powers vector-based and metadata-based retrieval
• This enables high-leverage workflows across data curation, training set construction, and ML research
• You’ll work closely with the Technical Lead and peers to scale and evolve the system that supports retrieval across hundreds of thousands of hours of driving data
• You’ll contribute to architectural decisions, own complex components, and help define best practices as the team grows
• You’ll play a key role in launching the next version of our indexing stack
• This will support basic similarity search and metadata filtering across hundreds of thousands of hours of video and sensor data
• The immediate focus is to move quickly
• You’ll leverage off-the-shelf tools, help inform build vs. buy decisions, and deliver an MVP system in months, not quarters
• This role requires someone who is comfortable with ambiguity, technically pragmatic, and able to make strong architectural decisions in imperfect conditions
• You’ll collaborate with ML teams, platform engineers, and downstream users to turn indexing from a bottleneck into a core capability
• Build and operate production search and indexing systems at billion-vector scale
• Apply and evaluate embedding models for semantic and multimodal retrieval, including inference, normalization, versioning, and integration
• Build evaluation frameworks to measure retrieval quality, recall, relevance, filtering accuracy, latency, freshness, scalability, and cost
• Develop backend services and APIs for vector similarity and metadata-filtered search
• Own search components from design and implementation through deployment and production operations
• Collaborate with ML, Data, and Evaluation teams to integrate search into training, curation, and evaluation workflows
• Support the growth of other engineers through code reviews, technical guidance, knowledge sharing, and mentoring