> Markdown version of [/jobs/ext/4616996-senior-machine-learning-engineer-search-index](https://www.wearedevelopers.com/jobs/ext/4616996-senior-machine-learning-engineer-search-index). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer, Search & Index - **Company:** WAYVE LLC - **Location:** Sunnyvale, CA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LanceDB, Application Programming Interfaces (APIs), Big Data, Distributed Data Store, Python (Programming Language), Machine Learning, Metadata, Systems Integration, Large Language Models, Backend, Low Latency, Machine Learning Operations, FAISS, Data Selection - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_be07ad4436447c4885e100413ac512f2 ## About the Role * 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 ## Description * 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