Interested in this AI/ML Engineer role at XiFin?
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Are you interested in harnessing technology and AI to transform healthcare?
At XiFin, we believe a healthier, more efficient healthcare system starts with strong financial and operational foundations. Our innovative technologies help diagnostic providers, laboratories, and healthcare systems manage complexity, drive better outcomes, and stay focused on what matters most: patient care.
We’re on a mission to simplify the business side of healthcare—and we know that mission takes people from all backgrounds and experiences. Whether you’re early in your career or bringing years of expertise, we welcome your perspective, your curiosity, and your passion. We value individuals who ask questions, challenge the status quo, and want to grow while making a real difference.
About the Role
AI Data Analyst to support the design, deployment, and optimization of AI\-driven solutions for healthcare revenue cycle management (RCM). This role sits at the intersection of business operations, data analytics, and AI engineering within AI team, acting as a liaison between business stakeholders and technical teams.
The ideal candidate combines strong analytical capabilities with an understanding of healthcare revenue cycle workflows and has the ability to translate operational requirements into structured inputs for AI models and engineering teams.
In addition to analytics responsibilities, this role will support AI program coordination, helping manage initiatives related to Agentic AI platforms, AI product capabilities, and roadmap execution. The position requires someone who can work across teams to ensure AI solutions align with business priorities and operational needs.
Experience with RCM workflows such as billing, claims processing, denial management, and appeals is highly desirable.
This is an onsite position located at our office in San Diego, CA.
This position is not eligible for employment sponsorship now or in the future. Applicants must have current and ongoing authorization to work in the United States.
How you will make an impact:
In this role, you’ll:
Business \& AI Liaison
- Serve as a bridge between business stakeholders and AI/engineering teams, translating operational requirements into technical specifications and data requirements.
- Work closely with RCM operations teams to identify opportunities where AI can improve efficiency, accuracy, or automation.
- Communicate AI capabilities and limitations in business\-friendly language to non\-technical stakeholders.
- Facilitate requirement gathering sessions and document business processes for AI solution development.
AI Program Coordination
- Support the planning and coordination of AI initiatives, including projects related to Agentic AI platforms and AI\-driven workflow automation.
- Assist in tracking AI product development milestones, roadmap execution, and program deliverables.
- Work with AI architects, data scientists, and engineering teams to ensure business requirements are clearly understood.
- Monitor progress of AI initiatives and help coordinate cross\-functional collaboration across teams.
Data Analysis \& Operational Insights
- Analyze operational data related to claims, denials, appeals, prior authorization, and payer policies.
- Identify patterns and insights that can inform AI model development or workflow improvements.
- Support AI model evaluation by helping define business\-level success metrics and KPIs.
- Work with data engineering teams to validate datasets and ensure data quality for AI workflows.
AI Product \& Workflow Support
- Assist in defining and refining AI\-driven workflow designs, including AI\-assisted appeals generation, policy interpretation, and automated decision support.
- Support documentation of AI product features, data inputs, and operational workflows.
- Contribute to the development of AI product requirements and functional specifications.
What you will bring to the team:
We’re looking for someone with a growth mindset and a passion for learning. You might be a great fit if you:
- Strong problem\-solving mindset, with the ability to connect business challenges in revenue cycle operations to practical AI and data\-driven solutions.
- Collaborative and relationship\-focused, working effectively across business, product, engineering, and data teams to move initiatives forward.
- Curiosity and continuous learning, staying engaged with evolving AI capabilities and exploring how they can improve operational workflows.
- Clear and thoughtful communicator, able to translate complex technical concepts into practical insights that support business decision\-making.
Skills and experience you have:
You don’t need to check every box. We will consider a combination of education and experience, including:
Required Qualifications
- Bachelor’s degree in Data Analytics, Health Informatics, Business Analytics, Computer Science, or a related field.
- 4\+ years experience working in Healthcare Revenue Cycle Management (RCM) environments.
- Strong analytical skills with experience working with data\-driven operational workflows.
- Ability to translate business problems into structured analytical requirements.
- Strong communication skills with the ability to collaborate with both technical and non\-technical stakeholders.
- Experience working in cross\-functional environments involving engineering, product, and business teams.
Preferred Qualifications
- Familiarity with workflows such as claims processing, denials management, appeals, payer policies, and prior authorization.
- Exposure to AI / machine learning solutions in operational environments.
- Basic understanding of LLMs, AI agents, or Agentic AI architectures.
- Experience supporting technical programs or product development initiatives.
Key Skills
- Data Analysis
- Healthcare Revenue Cycle Management (RCM)
- Business Process Analysis
- AI Program Coordination
- Stakeholder Communication
- Requirements Gathering
- Cross\-functional Collaboration
Why XiFin?
We’re more than just a healthcare technology company—we’re a team that cares about people.
Here’s a glimpse at what we offer:
- Comprehensive health benefits including medical, dental, vision, and telehealth
- 401(k) with company match and personalized financial coaching to support your financial future
- Health Savings Account (HSA) with company contributions
- Wellness incentives that reward your preventative healthcare activities
- Tuition assistance to support your education and growth
- Flexible time off and company\-paid holidays
- Social and fun events to build community at our locations!
Pay Transparency
At XiFin, we believe in pay transparency and fairness. The expected annual salary range for this role is: $100,000\- $145,000\.
Final compensation will be determined during the selection process and may vary based on experience, skills, and geographic location.
Accessibility \& Accommodations
We’re committed to providing an inclusive and accessible experience for all applicants. If you need a reasonable accommodation during the application process, please contact us at 858\-436\-2900\.
Equal Opportunity Employer
XiFin is proud to be an equal opportunity employer. We value diverse voices and do not discriminate on the basis of race, color, religion, national origin, gender, gender identity, sexual orientation, disability, age, veteran status or any other basis protected by law.
Ready to apply?
We’d love to hear from you—even if you’re not sure you meet every qualification. If you're excited about the role and believe you can contribute to our team, please apply. Let's build something meaningful together.
Salary Context
This $100K-$145K range is in the lower quartile 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 XiFin, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($122K) sits 32% below the category median. Disclosed range: $100K to $145K.
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.
XiFin AI Hiring
XiFin has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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