Senior Machine Learning Engineer, Search & Index

Overview

In this role you will design, build, and operate a search and indexing platform for vast multimodal data, starting as the first hire in the Sunnyvale team. You will collaborate with ML, Data, and Platform groups to enable data curation, training set construction, and research workflows at scale. You’ll evaluate what to build vs. buy and move fast to deliver an MVP. Your work will impact how data and embeddings power autonomous driving research and deployment.

Pay / Benefits

• meaningful equity
• relocation support and visa sponsorship
• hybrid working model
• learning and development budgets
• comprehensive benefits including health insurance, dental, enhanced maternity/paternity leave, retirement/pension
• wellbeing partnerships and team socials

Responsibilities

• Design, build and operate the search and indexing platform for hundreds of thousands of hours of video and sensor data
• Scale systems beyond 10B vectors and own components from design through production
• Collaborate with ML, Data, Evaluation and Platform teams; mentor other engineers and participate in code reviews
• Develop backend services and APIs for vector similarity, semantic retrieval and metadata-filtered search
• Evaluate and integrate embedding models, vector databases, ANN libraries and external vendors
• Contribute to evaluation frameworks measuring retrieval quality, latency, scalability and cost
• Support integration of search into training-set construction, data curation and research workflows

Key requirements

• 7+ years in backend, infrastructure or ML systems
• Hands-on experience with vector databases or ANN technologies (e.g., FAISS, LanceDB)
• Understanding of multimodal embeddings and evaluation of search quality (relevance, recall, filtering accuracy, performance)
• Proficient in Python or other systems languages
• Built search services, APIs, and large-scale ingestion/indexing pipelines
• Experience delivering systems in ambiguous problem spaces
• Comfortable working across infra, ML and data curation

• collaboration across cross-functional teams
• mentoring and code-review support
• strong problem-solving and communication
• vector databases/ANN (FAISS, LanceDB)
• multimodal embeddings
• search services and APIs

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