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About This Role
About Karbon
Karbon is the global leader in AI-powered practice management software for accounting firms. We provide an award-winning cloud platform that helps tens of thousands of accounting professionals work more efficiently and collaboratively every day. With customers in 40 countries, we have grown into a globally distributed team across the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well-funded, ranked #1 on G2, growing rapidly, and have a people-first culture that is recognized with Great Place To Work® certification and on Fortune magazine's Best Small Workplaces™ List.
Karbon is further ahead on AI adoption than most companies our size. Every department is running AI daily. Hundreds of skills and workflows have been built by our own team. Fourteen department champions drive adoption and share knowledge across the business. The foundation is real and it works.
The next phase is scale. We need one person whose entire job is extending what we have built, measuring what it returns, and adding the next layer of capability. Not a strategy deck. A working system, the governance that makes it trustworthy, and the numbers that prove it.
You will report into the CEO and work across every department. You will have the access and sponsorship to make changes stick.
About the Role
Build the Tooling
- Ship internal automations and agents that remove repeated manual work across Sales, CS, Implementation, Support, and Operations. Connect AI to the systems we already run, through APIs and MCP servers.
- The first build is the data gateway. Karbon runs its own MCP server. You extend it into a routing proxy that sits between Claude and every internal data source teams need. Authentication via Okta. RBAC enforcement by department. Request logging for spend attribution and audit. Once the gateway is approved by Security, new connectors plug into a config entry instead of triggering a full review each time. That is your first thirty days.
- After that: CS gets a renewal risk signal. Sales gets a proposal draft automation. Implementation gets a SOW generator with scope-creep flags. Support gets a ticket triage router.
- Prototype fast. Harden what people use. Delete what they ignore. Hand every tool to an owner in the team that uses it. You build it. They run it.
Own Governance and Visibility
- Manage the Claude access and provisioning workflow — seat requests, model and tool access, connector approvals — and the Slack workflows that support it. Run the connector security review pipeline in partnership with Security.
- Own the spend dashboard across all AI tools, not just Claude. Cursor, Codex, Linear AI, and anything else the org is running sit alongside Claude in one view. Department leaders see their own number weekly without asking you to pull it. Bad trends surface before they become invoice surprises.
- Extend that visibility into an ROI framework: adoption rate, hours saved, cost per seat, project-level impact. Partner with Finance on budget accountability.
- Run the AI champions program. Fourteen department champions, organized into pods, own peer enablement and skill submissions within their teams. You run the cadences, maintain the hub pages, and hold champions accountable for engagement. When people leave Karbon, their projects and skills do not leave with them. You own the offboarding flow that captures and reassigns that work.
- Build and maintain the AI wiki — per-department documentation of tools, active use cases, and skills in use. Keep it current as tools evolve.
Train Teams and Set the Standard
- Work with each department lead to agree how their team uses AI, then hold that standard.
- Run practical training. Short, specific, and repeatable. Teach people the efficient way to get the same answer. Most cost problems are habit problems. After month one, you know what each team's workflows look like because you built them. That makes you a better trainer than anyone who has not.
- Build and maintain a shared library of prompts, skills, and patterns so knowledge stays when a person leaves. Own the pipeline that moves skills from intake through review to the shared library — and automate the parts of it that should not require a human.
Measure and Report the Return
- Define the small set of metrics that show whether AI is working: cost, adoption, delivery, and quality.
- Build the data pipelines behind those metrics. Expect the source data to be messy. Publish a regular report to the executive team. Same format every time. Bad news included.
- Delete any metric that has not changed a decision in a quarter.
About You!
- You shipped production software in the last two years, as the person writing the code. Python or TypeScript, SQL, and REST APIs.
- You built something real on top of a large language model. Not a demo. Something with users, error handling, and a cost you had to control.
- You worked directly with non-technical teams as the technical person in the room. Forward deployed engineer, solutions engineer, GTM engineer, internal tools engineer, or similar. You know what a CS renewal motion looks like and what implementation scoping feels like. You do not need to have worked in those roles. You need to have built something those teams actually used.
- You can build a number out of messy data and defend it to a CFO.
- You have run a cross-functional program where the participants did not report to you — a champion network, an ambassador program, an adoption initiative. You know how to create accountability without authority.
- You write clearly, and you can say no to work that will not pay off.
Bonus Points:
Accounting, professional services, or B2B software market experience. Familiarity with MCP server development, Claude API, or agent frameworks. Experience with Okta or similar identity providers.
What Success Looks Like!
By month six:
Three automations run in production, each owned by the team that uses it. Every department leader can see their own AI usage and cost, including spend outside Claude. The spend dashboard is live and trusted. Your first report to the executive team is argued with, not just acknowledged. The gateway has Security sign-off and every new connector goes through it.
By month twelve:
Cost per unit of work delivered is moving in the right direction, and everyone agrees on how it is calculated. Three departments extend and run their own tools without you. New starters learn our way of working with AI as part of onboarding. Champion pods are submitting and reviewing skills without you in the loop. The AI wiki is maintained by department owners, not by you.
Why Work at Karbon?
- Gain global experience across Australia, New Zealand, UK, and Canada
- Strong benefits package including:
- + Flexible Time Off with an encouraged 4 weeks use per year + Company paid medical for you and eligible spouse/partner and dependents + Paid dental and vision and eligible spouse/partner and dependents + 401(k) with company matching + Flexible Spending Account + Up to 8 weeks paid parental leave + Work-from-home stipend
- Work with (and learn from) an experienced, high-performing team
- A collaborative, team-oriented culture that embraces diversity, invests in development and provides consistent feedback
- Be part of a fast-growing company that firmly believes in promoting high performers from within
Please be aware that Karbon will only contact you via email from our domain, karbonhq.com. If you receive an email from any other domain, please do not click any of those links.
Karbon embraces diversity and inclusion, aligning with our values as a business. Research has shown that women and underrepresented groups are less likely to apply to jobs unless they meet every single criteria. If you've made it this far in the job description but your past experience doesn't perfectly align, we do encourage you to still apply. You could still be the right person for the role!
We recruit and reward people based on capability and performance. We don't discriminate based on race, gender, sexual orientation, gender identity or expression, lifestyle, age, educational background, national origin, religion, physical or cognitive ability, and other diversity dimensions that may hinder inclusion in the organization.
Generally, if you are a good person, we want to talk to you.
If there are any adjustments or accommodations that we can make to assist you during the recruitment process, and your journey at Karbon, contact us at [email protected] for a confidential discussion.
At this time, we request that agency referrals are not submitted for this position. We appreciate your understanding and encourage direct applications from interested candidates. Thank you!
Salary Context
This $180K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Karbon, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $180K to $190K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Karbon AI Hiring
Karbon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $190K - $190K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
Frequently Asked Questions
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