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About This Role
Who We Are:
At Emburse, you’ll not just imagine the future – you’ll build it. As a leader in travel and expense solutions, we are creating a future where technology drives business value and inspires extraordinary results. Our AI\-powered platform helps organizations modernize financial operations, increase visibility, and optimize spend across the enterprise.
The AI Automation Analyst (Claude) will design, build, and scale AI\-powered workflows using Claude to drive efficiency, consistency, and enhanced customer experiences across the CX organization. This role will act as the central owner of automation initiatives, identifying high\-impact use cases and translating them into production\-ready workflows that augment Customer
Success, Support, Renewals, Sales, and Operations teams.
The analyst will partner cross\-functionally to operationalize AI capabilities, ensuring solutions are aligned with business goals such as productivity gains, CSAT improvements, revenue generation, and churn reduction. This role will also establish governance, best practices, and performance tracking for AI\-driven automations.
### What you'll do
- End\-to\-End AI Strategy: Own and manage AI automation initiatives across the entire Customer Experience (CX) lifecycle, from onboarding and adoption to renewals.
- Claude Workflow Engineering: Design, build, and deploy sophisticated Claude\-based workflows to automate customer intelligence, health scoring, and repetitive CX processes.
- Revenue Orchestration: Automate the integration of customer insights into core business drivers, including renewals, upsells, and cross\-sell opportunities.
- AI Insight Extraction: Develop automated frameworks for high\-level data synthesis, including NPS analysis, churn patterns, and insight extraction from customer interactions.
- Prompt Engineering Leadership: Create and maintain scalable prompt engineering frameworks and reusable templates to ensure the consistency and accuracy of AI outputs.
- Cross\-Functional Integration: Collaborate with Product, Sales, and Ops to seamlessly integrate AI workflows into existing tech stacks, including CRMs and support platforms.
- QA \& Brand Governance: Establish rigorous quality assurance processes to validate AI outputs for compliance, accuracy, and alignment with the corporate brand voice.
- Performance Optimization: Monitor and tune automation performance against key business metrics such as NRR (Net Revenue Retention), Retention Rate, and CSAT.
- Enablement \& Governance: Act as the internal Subject Matter Expert (SME) for AI capabilities, providing documentation, stakeholder training, and ensuring responsible AI data privacy and risk mitigation.
### What you'll bring
- Education: Bachelor’s degree in Business, Analytics, Computer Science, or a related technical field (Master’s in AI/ML preferred).
- Core Experience: 2–4 years of proven success in CX operations, AI automation, or high\-level data analytics.
- LLM Proficiency: Hands\-on experience with Claude or other LLMs, including a strong background in prompt engineering and workflow design.
- Tech Stack Expertise: Familiarity with core CX platforms and integrations, including Salesforce, Pendo, Slack, and automation tools like Zapier.
- Systems Thinking: Strong analytical ability to translate complex business problems into scalable, automated AI solutions.
- Technical Literacy: Solid understanding of APIs, data integrations, and the infrastructure required for seamless AI workflow deployment.
- Strategic Communication: Excellent ability to explain technical AI capabilities, limitations, and governance to non\-technical stakeholders.
- Operational Rigor: Demonstrated skill in balancing rapid execution with high\-quality output, thorough documentation, and risk mitigation.
Why Emburse?
Finance is changing—and at Emburse, we’re leading the way. Our AI\-powered solutions help organizations eliminate inefficiencies, gain real\-time visibility, and optimize spend—so they can focus on what’s next, not what’s slowing them down.
- A Company with Momentum – We serve 12M\+ users across 120 countries, helping businesses modernize
their finance operations.* A Team That Innovates – Work alongside some of the brightest minds in finance, tech, and AI to solve real\-
world challenges.* A Culture That Empowers – Competitive pay, flexible work, and an inclusive, collaborative environment that
supports your success.* A Career That Matters – Your work here drives efficiency, innovation, and smarter financial decision\-making
for businesses everywhere. Shape your future \& find what’s next at Emburse.
Emburse provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Emburse complies with applicable state and local laws governing nondiscrimination in employment in every location where the company has facilities. This policy applies to all terms and conditions of employment.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Emburse, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Emburse AI Hiring
Emburse has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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