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About This Role
About Cardiff
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Cardiff lends to small businesses. Since 2004 we've funded more than $12 billion across the country, to businesses in logistics, healthcare, construction, professional services, and plenty in between. We're building our own underwriting technology, and we still answer the phone; our customers need capital fast, and they want a straight answer from a person who can actually help.
Location: Remote (U.S.)
Employment Type: Full\-time
Department: Engineering
Why This Role Matters
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Cardiff's edge is how quickly we can convert a lead into a funded customer. Today our average turnaround is 24 business hours; our goal is minutes, through automated systems with human\-in\-the\-loop oversight. AI\-native engineering has changed what one strong engineer can deliver, and we're staffing accordingly: not more headcount doing traditional work, but engineers who have rebuilt how they work around modern tooling and ship in days what used to take weeks. The work you do here goes straight into the systems that will get us there.
The Role
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You'll own vertical projects end to end: the customer\-facing products and internal tools that sit on top of Cardiff's shared platform. You'll report directly to the Managing Partner, work with our Lead AI Architect on system design, and sit with the stakeholders behind each project (operations, sales, underwriting, marketing, finance) to turn what they need into working product, often inside a week. The team is small and senior. Your work will be in production your first week, and what you own will grow as fast as you show you can carry it.
What Success Looks Like
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In your first 90 days, you will:
- Ship a product to production that materially beats the status quo for the department you serve, measured on the KPI that fits: speed, accuracy, ROI, or conversions
- Take at least two more prototypes to production with clearly defined before\-and\-after KPIs
- Build positive working relationships with stakeholders
- Learn Cardiff's business model and integration surface well enough to ship against it without hand\-holding
By the end of your first year:
- A portfolio of production systems you own, with attributable impact on funding volume, conversion, or operating cost
- Faster product cycles across the team, with AI\-assisted development patterns you introduced now in common use
- A track record of judgment: systems that scale, and AI\-generated code that got read, audited, and corrected rather than merged on faith
What You'll Do
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- Own each project end to end: backend, frontend, deployment, and iteration once it's live
- Design and launch AI agents that talk to customers, tied to results you can measure
- Work with the Lead AI Architect on architecture and the ongoing evolution of Cardiff's AI platform
- Build against our shared integrations: Salesforce, Snowflake, Gmail, Zoom, Slack, and internal tools
- Reach for best\-in\-class open source before building from scratch: where the work isn't core IP, adopting a strong community project (think Buzz, the agent\-native workspace Block recently open\-sourced) beats owning the support and upgrade burden of a homegrown clone
- Make AI tooling your default way of building, and read what it produces before it ships
- Ship a working v1 today and improve it Monday, instead of a "perfect" v1 a month from now
What We're Looking For
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- 5\+ years of professional software engineering, with senior\-level ownership of production systems
- Production features shipped using Claude Code, Cursor, Codex, Devin, or similar as your primary way of building, not occasional experimentation
- A recent example (within the last six months) of using AI tooling to deliver something 3–5x faster than you could have without it
- Range across the stack: Python, modern JavaScript/TypeScript, SQL, REST/GraphQL, and cloud deployment (AWS preferred)
- Depth in agent orchestration (loops, graphs, multi\-step tool use, multi\-agent patterns; LangGraph or similar), with LLM APIs, RAG and vector databases, prompt engineering, and evals running in production, not just prototypes
- Build\-vs\-adopt judgment: fluent enough in the open\-source AI ecosystem to reach for a strong community project rather than hand\-rolling what already exists
- The engineering fundamentals to recognize when AI output is wrong
Nice to have:
- Salesforce APIs, Snowflake, or fintech/lending data models
- Contributions to open\-source AI projects
- Experience shipping solo or on a small founding team
Who Does Well Here
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You ship: a rough v1 in production beats a polished v1 in staging. You're fine with priorities that move. You work independently and communicate proactively; when something is blocked, you say so the same day.
You know the frontier better than we do. We're not looking to teach you the latest capabilities; we're looking to be wowed, and to learn from you.
We care less about the brand on your resume than what you've shipped in the last 90 days. A self\-taught engineer with a GitHub full of working AI\-native projects beats a 15\-year veteran just starting to explore these tools. Show us the work.
This Role May Not Be a Fit If You
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- Prefer structured corporate environments with clear lanes
- Want detailed direction before you start
- Would rather maintain systems than build them
- Use AI tools to assist your old workflow, rather than having rebuilt your workflow around them
Benefits
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- Medical, dental, and vision coverage
- 401(k) plan
- Flexible PTO and observed holidays
- Remote\-first, with support for your home\-office setup
- Direct access to leadership and to the problems that matter
How to Apply
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Email AI\[email protected] with:
- A 2–3 minute video introducing yourself and walking us through your work
- A link to your GitHub
- A specific project you've shipped to production, with a link we can see or try
Cardiff, Inc. is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace and do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable law.
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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Cardiff, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cardiff AI Hiring
Cardiff has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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