Interested in this AI Software Engineer role at JOBBER?
Apply Now →About This Role
Are you driven to bring people, technology, and strategy together to build impactful software?
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Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber, they can quote, schedule, invoice, and collect payments from their customers while providing an easy and professional customer experience. Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. That’s why we put the power and flexibility in their hands to run their businesses how, where, and when they want!
THE PROBLEM YOU’D OWN
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Jobber has AI in production, but not yet at its full potential.
We already have AI answering calls, drafting responses, and powering parts of our product. But today, those systems are still fragmented. Some teams are ahead. Others aren’t. Some workflows are intelligent. Others are still manual. And most importantly, the system doesn’t yet *think* across the product.
A service pro still has to:
- Manually follow up on jobs
- Piece together context across workflows
- Decide what to do next
The platform doesn’t proactively help them run their business. That’s the gap.
The opportunity is to evolve Jobber from: AI\-powered features AI\-powered workflows AI\-powered business operations
This role owns that shift. Not a team. Not a feature. The system.
THE CUSTOMER
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You’re building for people who don’t have time to think about software.
- A plumber finishing their last job at 6 pm
- A cleaner managing 30 clients and 5 employees
- A landscaper juggling scheduling, payments, and follow\-ups
They’re not asking for “AI.” They’re asking:
- “What should I do next?”
- “Why didn’t this job convert?”
- “Who should I follow up with today?”
And eventually:
- They shouldn’t have to ask at all.
The Director who succeeds here will understand:
This isn’t about building clever systems; it’s about building systems that remove thinking from already overwhelmed people.
WHAT YOU’D OWN
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End\-to\-end ownership of Jobber’s AI system layer. You’re not owning a single team. You’re owning how intelligence flows across the entire product.
### Product \+ Platform Scope
- AI Foundations (models, orchestration, evals, guardrails)
- Copilot (user\-facing intelligence layer)
- Automations (workflow execution layer)
- Platform Experience / Marketplace (integration \+ ecosystem surface)
- Emerging surfaces (voice, messaging, cross\-product intelligence)
### What this actually means
You are responsible for:
- How decisions get made inside the system
- How context moves across workflows
- How actions get triggered (and when they shouldn’t)
- How we evaluate whether AI is *actually working*
This includes:
- Agentic workflows (reason decide act evaluate)
- Cross\-product context (jobs, customers, payments, communication)
- Reliability, safety, and failure modes
- Developer experience for building on top of AI systems
### Team Structure
- \~30 engineers across 4–6 teams
- 4–6 EMs / Sr EMs reporting into you
- Close partnership with Product, Design, Data
WHAT “GOOD” LOOKS LIKE
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Not “we shipped AI features.”
Instead:
- The system proactively recommends and takes actions
- Teams build on shared AI primitives, not reinventing them
- AI output is reliable, measurable, and improving over time
- Engineers trust the system, and move faster because of it
- Customers feel like the product is *working for them*, not just responding
THE AI BAR (THIS ROLE IS DIFFERENT)
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We are not looking for:
- Someone who rolled out Copilot internally
- Someone who used LLM APIs for features
- Someone adjacent to AI
We are looking for someone who has:
Built real systems where AI makes decisions and takes actions in production.
That means experience with:
- Agent orchestration (not just prompts)
- Tool use and workflow execution
- Evaluation (offline \+ online)
- Observability and failure handling
- Guardrails and safety in real systems
- Tradeoffs between autonomy vs. control
You don’t need to code daily, but you must be able to reason at the system level.
WHAT YOU’LL ACTUALLY DO
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- Define how AI should work across Jobber, not just within a team
- Build and evolve a multi\-team org to execute on that vision
- Make tradeoffs between speed, quality, and safety
- Push teams beyond feature thinking into system thinking
- Challenge assumptions, including leadership’s
- Drive adoption across engineering, product, and the company
WHAT WE’RE LOOKING FOR
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### Leadership
You’ve led orgs through complexity, not just growth.
- Managed managers across multiple teams
- Built organizations that scale (not just teams that ship)
- Driven cross\-org alignment in ambiguous spaces
### Product \+ Systems Thinking
You think in systems, not features.
- You understand how user workflows connect end\-to\-end
- You’ve partnered deeply with Product and Design
- You care about customer outcomes, not just technical output
### AI Depth (non\-negotiable)
You’ve built or led production LLM/agentic systems.
- You understand what actually works (and what doesn’t)
- You’ve seen systems fail and improved them
- You have opinions about evaluation, reliability, and safety
### Execution
You can move fast without breaking everything.
- You’ve balanced shipping vs infrastructure vs tech debt
- You know when to iterate and when to redesign
WHY JOBBER · WHY NOW?
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This is not “AI theatre.”
We already have:
- AI Receptionist (live, handling real customer calls)
- AI features embedded across the product
- 250,000\+ businesses using the platform
What we don’t have yet is: A unified, intelligent system across the product.
That’s what this role builds.
TLDR:
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Most Director roles optimize delivery. This one defines: How an entire product becomes intelligent.
What you can expect from Jobber:
- A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, matching in RRSP, TFSA or FHSA, and stock options.
- A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential.
- A unique opportunity to build, grow, and leave your impact on a $400\-billion industry that has no dominant player...yet.
- To work with a group of people who are humble, supportive, and give a sh\*t about our customers.
*We believe that diverse teams perform better and that fostering an inclusive work environment is a key part of growing a successful team. We welcome people of diverse backgrounds, experiences, and perspectives. We are an equal opportunity employer, and we are committed to working with applicants requesting accommodation at any stage of the hiring process.*
A bit more about us:
Job by job, we’re transforming the way service is delivered. Your lawn care provider, home cleaning service, plumber or painter could use Jobber to better connect with their customers, save time in the office, invoice faster, and get paid! We’re bringing tens of thousands of people together with technology to deliver billions of dollars a year in services to happy customers. Jobber exists to help make these small businesses successful, and when they’re successful we all win!
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 JOBBER, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150.
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.
JOBBER AI Hiring
JOBBER has 1 open AI role right now. They're hiring across AI Software Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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