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About This Role
Kai is the AI company rebuilding cybersecurity for the machine\-speed era. Founded by second time founders and trusted by Fortune 500 enterprises, Kai is building a future where security has no categories, no silos, and no human speed bottlenecks. The Kai Agentic Platform replaces fragmented, human\-limited workflows with agentic AI systems that continuously contextualize, assess, reason, and execute security work at the speed of thought \- making human defenders, superhuman.
Why Kai?
- Well\-funded: With $125M raised, we have the capital, runway, and resolve to rebuild cybersecurity from first principles.
- Proven: We've earned the trust of Fortune 500 and Global 1000 companies, and we're just getting started. Their confidence in Kai reflects what we've built: an AI\-powered cybersecurity platform that performs at the scale and speed the enterprise demands.
- Experienced founders: Our founding team consists of second\-time entrepreneurs, each with over 20 years of experience in the cybersecurity industry. Their proven expertise and vision drive our ambitious goals.
- World\-class leadership team: Our Heads of AI, Engineering, and Product bring extensive experience from some of the world’s most influential companies, ensuring top\-tier mentorship, direction, and vision.
- Frontier AI Applied Research Team: Our researchers operate at the leading edge of agentic AI systems, translating breakthrough capabilities into real\-world cybersecurity applications.
- Generous compensation: We offer highly competitive salaries, equity options, and a supportive work environment. Your contributions will be valued and rewarded as we grow together.
About the Role
We are building an AI\-powered cybersecurity platform that helps enterprises manage vulnerabilities at scale. Our AI team has built working services that analyze container images, standardize package data, extract natural language filters, and assess package maintenance — all powered by LLMs and intelligent automation.
Now we need an experienced software engineer to make these systems scale. You'll be the first dedicated engineering hire on the AI team, working alongside applied AI scientists and an AI infrastructure engineer to transform code into reliable, well\-tested, and maintainable production services.
This is not an ML research role. This is a software engineering role on an AI team. You'll own the code quality, test coverage, CI/CD pipelines, and production reliability of services that call LLM APIs, interact with Azure cloud services, and serve critical data to our cybersecurity platform.
Key Responsibilities
- Build the test suite from the ground up. You'll design the test infrastructure — unit tests with mocked LLM responses, integration tests against staging environments, and fixtures that make testing fast and reliable. You'll wire this into CI so nothing ships without passing tests.
- Harden production services. Audit and fix security issues. Implement structured logging. Add health checks, metrics, and traces.
- Improve the CI/CD pipeline. You'll add quality gates so the team catches issues before they reach production.
- Refactor for maintainability. Extract shared patterns into reusable modules. Break apart oversized classes and reduce code duplication across services.
- Fix dependency management. Introduce lock files for reproducible builds, remove unused dependencies, and resolve version inconsistencies across services.
- Own the reliability and performance of our AI service fleet (Python/FastAPI microservices)
- Build out observability — distributed tracing, latency dashboards, alerting on error rates and SLA breaches
- Design and implement caching strategies, rate limiting, and circuit breakers for external API calls (Anthropic, Azure ML, package registries)
- Collaborate with AI scientists on prompt engineering and output parsing, bringing engineering rigor to LLM integration patterns
- Mentor mid\-level engineers as the team grows
Required Qualifications
- 4\+ years of professional software engineering experience with a strong backend focus
- Deep Python expertise — not scripting, but well\-structured production code. You understand when to use dataclasses vs Pydantic, how async/await actually works, and why global variables make testing painful
- Testing as a core discipline. You've built test suites for services with external dependencies. You're comfortable with pytest, mocking, fixtures, and know how to test code that calls third\-party APIs without calling them
- FastAPI or equivalent modern Python web framework experience (Django REST Framework, Flask with production patterns). You've designed and maintained REST APIs that other teams depend on
- Azure or equivalent cloud platform experience. You've worked with managed container services, Kubernetes, managed databases, identity/auth systems, and CI/CD in a cloud environment. Azure preferred; AWS/GCP experience transfers well
- CI/CD pipeline engineering. You've added test gates, lint checks, and automated quality enforcement to build pipelines. Experience with Azure DevOps Pipelines, GitHub Actions, or GitLab CI
- Docker and containerization. You've written production Dockerfiles, understand multi\-stage builds, and have debugged container networking and configuration issues
- Strong code review and collaboration skills. You'll be working with AI scientists who are strong in their domain but still developing engineering practices. You need to raise the bar without creating friction
Preferred Qualifications
- Experience working with LLM provider APIs (Anthropic, OpenAI, Azure OpenAI) — understanding token limits, prompt design, structured output parsing, and retry patterns
- Experience with structured logging (structlog), observability tools (OpenTelemetry, Prometheus, Grafana), or APM platforms
- Exposure to cybersecurity, vulnerability management, or compliance\-sensitive environments
- Experience on a small engineering team at a startup, where you owned services end\-to\-end
- Familiarity with RAG patterns, embedding pipelines, or vector databases (not required, but a plus for growth)
Why This Role
- High\-impact ownership. You'll build the engineering foundation for an AI platform that protects enterprises from security vulnerabilities.
- Growth into AI/ML engineering. As our AI capabilities mature into fine\-tuning, custom model serving, and evaluation frameworks, you'll grow into MLOps and AI infrastructure. We'll invest in your development.
- Shape the team. You'll have input into hiring decisions as we grow the engineering side of the AI team. The engineers we hire next will be your peers and reports.
- Work with cutting\-edge AI. You'll work daily with Claude, and other frontier models — not training them but engineering the systems that make them useful in production.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Rippling, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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