About this role
Software Engineer – Product
Location
San Francisco, CA
Work Arrangement: On-site, 5 days per week
Company Stage
Series A | Approximately 60 employees | AI / Insurance / Enterprise Software
Compensation
Base Salary: $180,000–$280,000
Equity: Competitive equity
Performance Bonus: Quarterly performance bonuses
Total Compensation: Total cash compensation can be significantly higher than base salary, depending on performance and the applicable bonus structure.
Visa
Open to visa transfers, including OPT and H-1B transfers.
Company Description
We are an AI-native commercial insurance brokerage using proprietary technology to transform how commercial insurance is sold and serviced.
Our platform combines insurance expertise with AI-powered automation across the entire brokerage lifecycle, from prospecting and quoting to binding policies and ongoing customer service. By embedding AI directly into business operations, the company is building scalable systems that help teams work faster, improve service delivery, and automate complex workflows.
The company has grown rapidly and is building technology that directly drives business performance. Engineers work closely with business and operations teams to identify real problems, develop practical solutions, and ship improvements quickly.
The engineering culture emphasizes speed, autonomy, measurable business impact, and the use of modern AI tools to build products that solve real operational challenges.
What You Will Do
Own business KPIs end-to-end: Take responsibility for one or more business metrics and build AI-powered product features that directly improve measurable outcomes.
Work directly with business stakeholders: Partner with Sales, Service, Underwriting, and other non-technical teams to identify operational problems and translate them into scalable software solutions.
Build AI-powered products: Design, build, and deploy agentic AI systems, retrieval-augmented generation (RAG) architectures, Model Context Protocol (MCP) integrations, and automation workflows.
Own solutions across the full stack: Take ideas from initial problem discovery through prototyping, implementation, deployment, and ongoing iteration.
Move at exceptional speed: Prototype, ship, test, and improve features in hours rather than days or weeks.
Build production-grade AI systems: Develop reliable systems using modern AI frameworks, APIs, cloud infrastructure, and agent architectures.
Support live production systems: Participate in on-call responsibilities, troubleshoot issues, and maintain the reliability of systems that power day-to-day business operations.
Use AI-native development tools: Leverage tools such as Cursor and Claude Code to accelerate development, improve iteration speed, and maintain high-quality code.
Drive business outcomes: Evaluate your work based on its impact on operational efficiency, customer experience, revenue, and the KPIs you own.
Ideal Candidate Background
Required Experience
4+ years of professional software engineering experience.
Experience building and shipping production software, preferably in an AI-native or fast-growing startup environment.
Strong product engineering skills, with the ability to take a problem from discovery to a deployed solution.
Experience building AI-powered applications, automation, or systems that solve real business problems.
Ability to work directly with non-technical stakeholders and translate business needs into technical solutions.
Demonstrated ownership of projects, features, or systems with measurable outcomes.
Comfort working at a very fast pace, iterating frequently, and shipping improvements continuously.
Willingness to support production systems and participate in on-call responsibilities.
Technical Skills
Python: Experience building backend services, automation, or AI-powered applications.
TypeScript: Experience building application logic, services, or full-stack product features.
LLMs and generative AI: Experience integrating large language models into practical applications.
Agentic AI: Experience building AI agents or multi-step workflows that use tools, APIs, and external systems.
RAG architectures: Familiarity with retrieval-augmented generation systems and the infrastructure required to make them useful in production.
MCP: Experience with or understanding of Model Context Protocol and tool integrations.
Cloud infrastructure: Experience deploying and operating applications on AWS, GCP, or comparable cloud platforms.
AI development tools: Familiarity with tools such as Cursor and Claude Code, or equivalent AI-assisted development environments.
Full-stack engineering: Ability to work across application layers and deliver complete product capabilities.
Production systems: Understanding of deployment, debugging, reliability, monitoring, and incident response.
Preferred Background
Experience at an AI-native startup or a company where AI is central to the product or operating model.
Experience building agentic systems that automate real business workflows.
Experience building internal tools or operational software used by business teams.
Experience working with voice AI or voice-enabled workflows.
Experience in insurance, financial services, or other complex, operationally intensive industries.
Experience owning a business metric and connecting engineering work to measurable outcomes.
Experience using AI coding tools to accelerate development and improve iteration speed.
Experience in a high-growth environment where requirements evolve quickly and engineers are expected to make decisions independently.
Working Style and Culture
High ownership: Takes responsibility for business outcomes, not just assigned tickets.
Extremely fast execution: Can prototype, ship, learn, and iterate in hours.
Business-oriented: Understands the operational problem behind a feature and measures success by its impact.
AI-native mindset: Uses AI as a core part of the development process and builds AI-powered solutions where appropriate.
Autonomous: Comfortable operating without extensive processes or constant direction.
Product-minded: Focuses on delivering useful, scalable solutions rather than building technology for its own sake.
Collaborative: Works effectively with non-technical teams and communicates technical concepts clearly.
Pragmatic: Makes trade-offs that balance speed, quality, reliability, and business value.
Comfortable with responsibility: Willing to support live systems and take ownership when production issues arise.
Compensation and Benefits
Base salary of $180,000–$280,000.
Competitive equity.
Quarterly performance bonuses.
Total cash compensation that can significantly exceed base salary, depending on performance and the applicable bonus structure.
Opportunity to build AI-powered products that directly influence business performance.
A fast-moving, AI-native engineering environment.
High ownership over product development and business outcomes.
Opportunity to work closely with Sales, Service, Underwriting, and other business teams.
Modern development stack, including Python, TypeScript, cloud platforms, agent frameworks, RAG, MCP, and AI coding tools.
Why Join?
Direct business impact: Own KPIs and build features that produce measurable improvements in revenue, efficiency, and service delivery.
Build AI into real operations: Develop agentic systems and automation that power a commercial insurance business.
Ship at exceptional speed: Work in an environment where ideas can move from problem discovery to production in hours.
Own the full lifecycle: Work directly with stakeholders, design solutions, ship them, and measure their results.
Use modern AI tools: Build with current AI frameworks, agent architectures, and AI-assisted development tools.
High ownership and autonomy: Make meaningful technical and product decisions without unnecessary layers of process.
Competitive compensation: Earn a strong base salary, equity, and performance-based bonuses.
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