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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 AI Platform replaces fragmented, human\-limited workflows with Agentic AI systems that continuously contextualize, assess, reason, and execute security work at machine speed \- making human defenders, superhuman.
Why Join 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 delivered real, working products — services that use LLMs for natural language filtering, container image analysis, package standardization, and maintenance assessment. The AI science is strong. Now, the AI team needs dedicated engineering leadership to match.
You will be the technical leader of the AI platform engineering team. This is a hands\-on architecture and leadership role. You'll set the engineering standards, design the system architecture, establish the testing and deployment practices, and build the team that takes our AI services from "it works" to "it scales, it's reliable, it's maintainable, and we can ship with confidence."
You'll work directly with the Head of AI, the applied AI scientists, and the backend engineering team. You'll report to engineering leadership and have a seat at the table for technical strategy decisions. This is not a management role — it's a technical leadership role where you write code, review architecture, and mentor engineers while setting the direction for how AI systems get built and operated.
Key Responsibilities
- Audit and roadmap. Assess the current services, architecture, and deployment practices. Produce an engineering roadmap that prioritizes the highest\-impact improvements (we'll give you a head start — we know where the gaps are).
- Establish engineering foundations. Design and implement the shared library architecture — common patterns for configuration, DB access, error handling, structured logging, and health checks that all services adopt. Define the coding standards, PR review process, and quality gates.
- Build the test infrastructure. Not just write tests but design the testing strategy: how we mock LLM APIs, how we run integration tests against staging, how we measure coverage, and how it all plugs into CI. Set the standard that the team follows.
- Consolidate and fix CI/CD. You'll design a parameterized pipeline template, add test and lint stages, and establish the deployment strategy (staging, canary, rollback).
- Fix critical production issues. You'll prioritize and address the highest\-risk issues while establishing patterns that prevent new ones.
- Own architecture decisions across the AI platform — API contracts, caching strategies, data flow, service boundaries, and technology choices
- Lead the engineering hiring process for the AI team — define the roles, conduct technical interviews, and help build a high\-performing team of 3\-5 engineers
- Mentor AI scientists on engineering practices — testing, code structure, version control workflows — in a way that improves their code without slowing their research velocity
- Design evaluation and reliability frameworks for LLM\-powered features — how we measure accuracy, detect regressions, and monitor production behavior
- Partner with backend engineering to define how AI services integrate with the core cybersecurity platform — API versioning, contract testing, SLAs, and data flow
- Translate business problems into technical architecture — work with product and AI leadership to turn ambiguous requirements into well\-scoped engineering work
Required Qualifications
- 8\+ years of professional software engineering experience, with at least 2 years in a technical leadership or staff\+ role where you set standards for a team or organization
- Deep software architecture expertise. You've designed shared libraries, defined API contracts, established coding standards, and made technology decisions that a team lived with for years. You think in terms of patterns, not just solutions
- Production systems ownership at scale. You've been paged at 2am, you've run incident postmortems, you've built the monitoring that catches problems before customers do. You understand what production\-grade actually means
- Strong Python expertise with emphasis on clean architecture — dependency injection, proper module boundaries, testable design, async patterns done correctly
- Testing leadership. You haven't just written tests — you've established testing culture. You've designed test strategies for systems with complex external dependencies
- Cloud platform expertise. Deep experience with Azure (preferred) or AWS/GCP. You've designed and operated containerized microservice architectures in production
- CI/CD and DevOps maturity. You've consolidated messy build pipelines, added quality gates, implemented deployment strategies (blue/green, canary), and established release processes
- Mentorship and technical leadership. You've raised the engineering bar on a team. You've done code reviews that teach, not just gatekeep. You've helped junior and mid\-level engineers grow. You can work with AI scientists who are domain experts but still developing engineering practices — and you can do it with empathy and patience
- Excellent communication skills. You'll interface with AI scientists, backend engineers, product managers, and executive leadership. You need to translate between these audiences fluently
Preferred Qualifications
- Experience working with or alongside AI/ML teams — you understand the workflow of AI scientists and how to build infrastructure that serves their needs without constraining their exploration
- Familiarity with LLM provider APIs (Anthropic, OpenAI, Azure OpenAI) and the engineering challenges of LLM integration (prompt management, output parsing, cost optimization, latency)
- Experience with Azure specifically — Cosmos DB, Container Apps, Azure Identity, Azure DevOps
- Background in cybersecurity, vulnerability management, or compliance\-sensitive environments (SOC2, data privacy)
- Track record of building engineering teams from early stage — you've been the first senior engineering hire and built the team around you
- Experience with RAG systems, embedding pipelines, vector databases, or LLM evaluation frameworks
- Exposure to MLOps practices — model versioning, experiment tracking, evaluation pipelines (this becomes increasingly relevant as we mature)
Why This Role
- Greenfield engineering leadership. You'll design the engineering foundation and set the standards from day one.
- Direct organizational impact. You'll build and lead a team, set the technical direction, and shape how AI gets delivered to customers. Your decisions will visibly move the company.
- AI without the hype. We're not chasing trends. We're building AI systems that solve real cybersecurity problems for real enterprises. You'll work with frontier LLMs in a domain that matters.
- Growth into AI/ML leadership. As the platform matures, this role evolves into leading the full AI infrastructure — model serving, evaluation, fine\-tuning pipelines, and MLOps. We'll invest in your growth alongside the platform.
- Startup impact, real product. We have customers, revenue, and a working product. You're not joining to figure out product\-market fit — you're joining to scale what works.
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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