Software Engineering Manager - Search

Verkada Verkada · Enterprise · Bayoffice · Cameras

Engineering Manager to lead Verkada's Search team, responsible for AI-powered search and computer vision capabilities. The role involves setting technical direction for search with embedding-based retrieval and agentic, multi-modal experiences powered by LLMs and VLMs. Responsibilities include owning production services (API, inference pipelines, vector databases), driving migrations, and managing a team of backend, frontend, and ML engineers.

What you'd actually do

  1. Build the Team: Recruit, hire, and mentor a high-performing group of backend and ML engineers covering search infrastructure, computer vision, and applied AI.
  2. Strategic Oversight: Own the end-to-end roadmap for Search and ML engineering backend from product-facing features like POI, LPR, and AI Search to the platform services that power them.
  3. Cross-Functional Partnership: Partner closely with Product, Design, CV/ML research, Camera Firmware, and Infrastructure teams to align on priorities, dependencies, and deployment plans.
  4. Agentic & Generative AI: Drive the rollout of AI-Powered Search, LLM migrations, VLM experimentation, and agentic AI into production-grade features.
  5. Embeddings & Retrieval: Oversee the evolution of our vector search stack to improve recall, latency, and cost at fleet scale.

Skills

Required

  • 7+ years of software engineering experience
  • 2+ years managing backend or ML engineering teams
  • Track record leading 5+ engineers running production services at meaningful scale
  • 4+ years of hands-on experience in at least two of: information retrieval, vector / embeddings-based search, computer vision, or large-scale recommendation systems
  • 2+ years productionizing modern LLMs, VLMs, or agentic systems (evals, guardrails, latency/cost tuning)
  • Deep proficiency in Python plus Go/Java/C++
  • fluent in distributed systems (gRPC, Kafka)
  • major cloud provider (AWS preferred)
  • Hands-on with at least one production vector database or search engine (OpenSearch, Turbopuffer, FAISS, Milvus, pgvector, etc.)
  • Strong operational instincts: on-call ownership, post-mortem rigor, and a habit of defining metrics and building evals to drive quality on high-availability services

Nice to have

  • Experience integrating foundation models via open-source GPU inference stacks
  • Familiarity with face recognition, person re-identification, LPR, or similar fine-grained CV pipelines
  • Background in physical security, video surveillance, or IoT/connected-device domains
  • CI/CD experience for ML/search services

What the JD emphasized

  • production services
  • LLMs
  • VLMs
  • agentic systems
  • vector database
  • search engine
  • high-availability services

Other signals

  • LLM
  • VLM
  • agentic experiences
  • vector search
  • computer vision