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About This Role
The Opportunity
As the Director, FP\&A and AI Finance at Coursedog, you'll own the financial engine of the business while helping transform how Finance operates through AI\-enabled workflows and automation. Working closely with executive leadership, Accounting, GTM, and business leaders across the company, you'll lead budgeting, forecasting, reporting, and strategic financial planning while building systems that accelerate insight delivery and reduce manual work. You'll be responsible for driving the full planning cycle, delivering actionable financial analysis, and leading AI\-driven process improvements across the Finance function. This role balances strategic thinking with hands\-on execution in a fast\-moving, mission\-driven B2B SaaS environment. This role reports directly to the CFO and offers autonomy leading projects in a remote environment. It's well\-suited for someone who enjoys taking ownership, thrives in ambiguity, moves quickly, and is motivated by creating scalable systems that help the company make better decisions.
If you're excited by the opportunity to combine world\-class FP\&A with emerging AI capabilities and want to help build a more intelligent, data\-driven finance organization, this is a chance to do meaningful work with significant impact.
This position offers a competitive base salary between $185,000 – $200,000, based on experience and qualifications. The role also includes a performance\-based variable compensation component, bringing total on\-target earnings (OTE) to $220,000 – $240,000 when performance objectives are achieved.
What You'll Do* Lead Coursedog's annual budgeting and financial planning process across all functions, ensuring alignment with company goals and strategic priorities
- Own monthly forecasting and financial performance tracking against budget
- Analyze business performance, identify key drivers of variance, and provide actionable recommendations to leadership
- Develop strong partnerships with business leaders to understand operational metrics and translate them into financial insights
- Lead monthly cross\-functional budget reviews, proactively identifying risks, opportunities, and areas for investment
- Develop and maintain executive and board\-level reporting that clearly communicates business performance and financial outlook
- Own GTM commission planning, tracking, forecasting, and reporting processes
- Partner with the Accounting team on procurement\-related activities, including vendor budgeting, spend management, and expense approval workflows
- Spearhead AI\-driven transformation initiatives across the Finance function, including automated workflows, agent\-based tools, and always\-on reporting capabilities
- Evaluate, prototype, and deploy AI\-enabled solutions that improve financial processes, reduce manual effort, and increase decision\-making speed
- Continuously improve FP\&A systems, processes, and reporting frameworks to support a growing SaaS business
- Serve as a trusted financial partner to executives and functional leaders across the company
Requirements
What You'll Bring* 7\+ years of FP\&A experience in a high\-growth SaaS environment, with increasing ownership and responsibility
- 2\+ years of investment banking or related rigorous financial training experience
- Demonstrated success leading budgeting, forecasting, and financial reporting processes in a fast\-paced organization
- Experience partnering with senior business leaders to influence decision\-making through data and financial insights
- Hands\-on experience applying AI tools and automation within finance workflows, including platforms such as ChatGPT, Claude, n8n, or similar technologies
- Ability to clearly articulate AI\-driven solutions you've built, the problems they solved, and their business impact
- Strong analytical and systems\-thinking skills with a focus on process improvement and operational efficiency
- Exceptional attention to detail and a commitment to producing high\-quality work
- Excellent written and verbal communication skills, with the ability to communicate effectively across technical and non\-technical audiences
- Proven ability to manage multiple priorities, meet deadlines, and operate independently in a remote environment
- Ownership mentality with a bias toward action and a willingness to operate across both strategic and execution\-focused work
- Experience working successfully in a fully remote or distributed organization is a plus
Not sure if you should apply? Research shows that women and people from underrepresented backgrounds are less likely to apply unless they meet every qualification listed. At Coursedog, we’re focused on finding the best person for the role, and that person may come from a non\-traditional background. We encourage you to apply even if you don’t meet every requirement — our evaluation focuses on your ability to thrive in this role and make an impact.
Benefits
About Coursedog
At Coursedog, our mission is simple but ambitious: to break down barriers to student success.
Founded by former college students who were frustrated by outdated academic systems, Coursedog was built to empower higher ed administrators to deliver exceptional educational experiences and make it easier for students to achieve their goals. Since then, we’ve grown into a platform trusted by institutions across the country.
Coursedog is the Intelligent Academic Operations Platform, empowering 400\+ colleges and universities to better serve over 3\.3 million students. Our platform brings together academic scheduling, curriculum and catalog management, assessment, and workflow automation — all designed to adapt to each institution’s unique needs and remove administrative roadblocks so educators can focus on helping students succeed.
We’re a remote\-first, fast\-growing company and a Forbes Top 500 U.S. Startup Employer, with a culture that prioritizes engagement, ownership, and impact. If you’re excited about solving meaningful problems, working with thoughtful teammates, and improving education at scale, you’ll feel right at home at Coursedog.
Working at CoursedogHealthcare, Dental \& Vision
We follow a fixed contribution model to promote equity and transparency. Every employee receives the same annual dollar amount to apply toward medical, dental, and vision coverage. This approach gives you the flexibility to choose the plan that best fits your needs, whether that is a base\-level option or more comprehensive coverage.
Depending on the selected coverage level, the company covers:
- Employee\-only coverage: 99% to 65% of monthly premiums
- Employee plus dependents: 77% to 50% of monthly premiums
We partner with Aetna and Guardian, and offer access to best\-in\-class wellness and support programs, including:
- Spring Health (mental health support)
- XP Health (vision benefits)
- Carrot Fertility (family planning support)
- One Medical (primary care)
We also support pre\-tax savings through Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA).
Retirement Planning
To support long\-term financial health, we offer a Safe Harbor 401(k) plan from day one of employment. Coursedog provides a 4% employer match on your total compensation (subject to federal limits) each pay period for employees contributing to a tax\-advantaged retirement account.
Paid Time Off
We trust our employees to manage their time responsibly. That’s why we offer an unlimited Paid Time Off policy — because flexibility and balance are critical for sustained excellence and personal well\-being.
Remote\-First Since Inception
We were built for distributed work. Our culture emphasizes flexibility over rigidity, outcomes over hours, and transparency over gatekeeping. We are committed to being a remote\-first organization indefinitely, empowering you to work where and how you thrive best.
Equity
As a valued contributor to our collective success, you’ll participate in the upside. All employees are granted equity in the company aligned to your role and tenure. Equity grants are reviewed and adjusted with promotions to reflect your evolving impact.
Paid Parental Leave
Coursedog supports all parents regardless of gender, family structure, or how you became a parent. All parents receive up to 6 weeks of Paid Bonding Leave at 100% of base pay following the birth, adoption, or foster placement of a child. Birthing parents receive an additional 6 weeks of Paid Medical Recovery Leave at 100% of base pay to support physical recovery following childbirth — for a total of up to 12 weeks of fully paid leave.
Salary Context
This $185K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Coursedog, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($192K) sits 6% above the category median. Disclosed range: $185K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Coursedog AI Hiring
Coursedog has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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