VP, AI-Native Ops & Automation, R&D
Algolia - Remote - United States
Posted May 21, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
VP, AI-Native Ops & Automation, R&D Remote - United States At Algolia, we're proud to be a pioneer and market leader in AI Search, empowering 17,000+ businesses to deliver blazing-fast, predictive search and browse experiences at internet scale. Every week, we power over 30 billion search requests - four times more than Microsoft Bing, Yahoo, Baidu, Yandex, and DuckDuckGo combined. In 2021, we raised $150 million in Series D funding, quadrupling our valuation to $2.25 billion. This strong foundation enables us to keep investing in our market-leading platform and serving incredible customers like Under Armour, PetSmart, Stripe, Gymshark, and Walgreens. To support this next phase of growth, this is a new individual contributor executive role responsible for transforming how Product, Engineering, AI Research, and Platform teams operate. Reporting directly to the CEO, the VP, AI-Native Ops & Automation, R&D will define and scale the operating system for how Algolia builds, ships, instruments, and continuously improves products and platforms across the company. This role goes beyond traditional Product Operations or Engineering Operations leadership. The mandate is to embed AI deeply into software development workflows, engineering coordination, prioritization systems, operational instrumentation, and organizational decision-making, enabling a high-velocity, continuously optimized R&D organization built for enterprise scale and rapid innovation. YOUR ROLE WILL CONSIST OF: Lead R&D Operating Model Transformation - Analyze and redesign the full product development lifecycle across planning, prioritization, development, deployment, and release management - Identify bottlenecks, coordination overhead, tooling gaps, and workflow inefficiencies across Product, Engineering, AI Research, and Platform
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