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Overlap

Founding AI Engineer

Salary$120k – $300k
LocationSan Francisco, CA, US
Work modeon-site
Typefull-time
EquityIncluded
Company size11+ people
First seen5d ago
Last seen5d ago
Founding AI Engineer at Kinro(P26)
Benefits
$120K - $300K • 0.75% - 2.00%
About the company
Autonomous insurance brokerage for small businesses
San Francisco, CA, USFull-timeWill sponsor3+ years
Apply now
About Kinro
Kinro helps companies sell insurance through AI assistants such as ChatGPT. We build AI sales agents that manage the end-to-end insurance sales process, from answering questions, personalized coverage explanations and quoting. These agents are continuously evaluated and improved through our proprietary platform, optimizing sales performance while remaining 100% compliant.
Selling insurance requires strict regulatory compliance, deep carrier integrations, and extremely reliable answers. Platforms like OpenAI and Google remain general-purpose and are less likely to build the vertical infrastructure required for regulated industries.
Today, Kinro serves brokers and direct-to-consumer carriers across the US and Europe. Insurance distribution is huge: insurers pay roughly $100B+ in commissions annually, and distributors spend $10B+ every year on advertising to acquire customers.
Pierre-Alexandre led research improving Gemini for financial services at DeepMind. Parth led infrastructure for training and inference at Zoox. Corentin was the first employee in an insurance startup and worked closely with many insurance leaders.
About the role
About Kinro
Kinro builds AI sales agents for the insurance industry, automating the full sales flow from qualification and quoting to recommendations and binding. Our north star is selling insurance in LLM platforms (ChatGPT, Gemini….)
Launch video:
https://www.youtube.com/watch?v=z_bC6nejbbg
What you’ll do
Responsibilities
You will help define how Kinro builds software in the age of agents. This is a hands-on role for someone who has built AI products end-to-end, trusts agentic coding as a force multiplier, and can ship across product, backend, evals, and infrastructure with strong systems judgment.
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Build AI product systems end-to-end, from tool use and backend services to evals and production monitoring.
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Use agentic coding tools aggressively and responsibly: break down large problems, set guardrails, and review generated work with high standards.
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Improve agent quality, latency, safety, and cost through better architectures, feedback loops, and evaluation harnesses.
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Work across the stack wherever leverage is highest, including product surfaces, infra primitives, and customer deployments.
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Turn one-off lessons from production into reusable product and platform capabilities.
What we’re looking for
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You have built AI products, agent workflows, or LLM systems end-to-end in production.
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You know when to trust an agent, when to intervene, and how to structure work so the agent succeeds.
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You understand tool calling, latency, reliability, security, and cost tradeoffs in agentic systems.
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You can reason across the stack: application code, APIs, databases, cloud systems, and operational tooling.
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You have strong product taste and care whether something feels simple, fast, and reliable for the end user.
Nice to have
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Experience with evals, model routing, prompt injection defenses, sandboxing, or other AI systems safety work.
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Experience in high-ownership product environments where engineers talk directly to users and ship quickly.
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Experience in fintech, insurance, or other regulated environments.
Hiring process
Interview Process
We aim to move quickly and keep the process practical.
1. Intro conversation (20–30 min)
A short call to understand your background, what you’ve built, and what you want next.
2. Technical / product deep dive (45–60 min)
We’ll talk through systems you’ve built end-to-end, including product decisions, architecture, tradeoffs, reliability, and how you shipped in production.
3. Founder / team conversations (45–60 min)
We’ll spend time on ownership, speed, taste, communication, and what it would look like to build together in person.
4. One week paid trial
You will join us on our day to day, working on real problems to see how you fit with the team.
5. References + final conversation
We’ll close quickly if there’s mutual fit.
We care most about people who can build useful products fast, reason clearly about tradeoffs, and raise the quality bar across the stack.
Apply now
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San Francisco, CA, USInternship$4K - $10K / monthly
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