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Verkada

Builds cloud-managed physical security systems

Software Engineering Manager - Search

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSan Mateo, CA, USAIn-person

About the Job

Requirements

  • At least 7 years of software engineering experience, including at least 2 years managing backend or machine-learning engineering teams.
  • A track record leading at least 5 engineers operating production services at meaningful scale.
  • At least 4 years of hands-on experience in at least two of information retrieval, vector or embeddings-based search, computer vision, or large-scale recommendation systems.
  • At least 2 years of productionizing modern large language models, vision-language models, or agentic systems, including evaluations, guardrails, and latency or cost tuning.
  • Deep proficiency in Python plus Go, Java, or C++; fluency in distributed systems, including gRPC and Kafka; and experience with a major cloud provider. AWS is preferred.
  • Hands-on experience with at least one production vector database or search engine, such as OpenSearch, Turbopuffer, FAISS, Milvus, or pgvector.
  • Operational experience with on-call ownership, post-mortem practices, defining metrics, and building evaluations for high-availability services.

Responsibilities

  • Recruit, hire, and mentor a high-performing group of backend and machine-learning engineers covering search infrastructure, computer vision, and applied AI.
  • Own the end-to-end Search and machine-learning engineering backend roadmap, from product-facing features such as POI, LPR, and AI Search to the platform services that power them.
  • Partner with Product, Design, computer vision and machine-learning research, Camera Firmware, and Infrastructure teams to align on priorities, dependencies, and deployment plans.
  • Drive the rollout of AI-powered search, large language model migrations, vision-language model experimentation, and agentic AI into production-grade features.
  • Oversee the evolution of the vector search stack to improve recall, latency, and cost at fleet scale.
  • Lead detection evaluation, model consistency, and ongoing quality improvements for computer vision and machine-learning pipelines.
  • Own a portfolio of production services, including submission endpoints, inference pipelines, APIs, and the database layer.
  • Execute in-flight migrations into new pipelines and deprecate old pipelines without disrupting customer-facing features.
  • Drive large-organization optimizations, inference-engine stability, gRPC load balancing, and connection-hardening work to maintain pipeline health as the fleet grows.
  • Own the team on-call rotation, post-mortem quality, and programs needed to scale the team.
  • Expand integration test coverage and search-pipeline change testing to catch regressions before production.
  • Define and track key metrics, including API latency, search quality, inference stability, and field reliability, using dashboards and service-level objectives.

Desired Qualifications

  • Experience integrating foundation models through open-source GPU inference stacks.
  • Familiarity with face recognition, person re-identification, license plate recognition, or similar fine-grained computer vision pipelines.
  • Background in physical security, video surveillance, or Internet of Things and connected-device domains.
  • Continuous integration and continuous delivery experience for machine-learning or search services.

About the Company

Verkada

Builds cloud-managed physical security systems

Verkada builds security cameras and cloud software for organizations that manage workplaces, stores, schools, and other physical sites. Its Command platform brings video security together with door access, alarms, intercoms, air quality sensors, and visitor management in one interface. Operators use a browser or mobile app to review incidents, manage entry permissions, receive alerts, and control equipment across locations. The company sells its devices with software licenses, which include updates and support, and its AI tools search footage and connect events across cameras. Verkada was founded in 2016 and is headquartered in San Mateo, California.

Company size
1,001-5,000
Company stage
Series E
Total funding
$643.9M
Headquarters
San Mateo, CA
Founded
2016

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