Senior Machine Learning Engineer, Search & Index
About The Position
We are hiring a Senior Machine Learning Engineer for our Search & Indexing team, 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 - enabling 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, supporting basic similarity search and metadata filtering across hundreds of thousands of hours of video and sensor data. The immediate focus is to move quickly: 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 in 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.
Requirements
- 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)
Nice To Haves
- Experience with large-scale ML pipelines or distributed data infrastructure.
- Familiarity with multimodal data, including video, images, sensor data, and metadata.
- Exposure to active learning, semantic retrieval, or training-data selection workflows.
- Prior experience in autonomy, robotics, or large-scale data infrastructure
- Experience evaluating technical vendors and working with external technology providers.
Responsibilities
- 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.
Benefits
- competitive equity package
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
