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About This Role
AI Developer, Implementations
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
As an AI Developer on the Implementations team, you'll design and build AI-powered solutions that improve how we onboard new customers and support them through go-live and beyond. You'll work closely with Implementations, Engineering, QA, and DevOps to develop intelligent tools, automate manual implementation processes, and evaluate emerging AI technologies that improve both engineering and implementation productivity.
This is a hands-on software engineering role for someone who enjoys building production-ready solutions, experimenting with new technologies, and solving complex technical challenges — with a particular focus on applying AI and automation to customer data conversion, implementation testing, and process improvement.
This position will be located at our offices in San Diego, CA.
How you will make an impact:
In this role, you’ll:
- Develop AI-powered applications, agents, and automation that improve implementation and software development workflows.
- Identify and execute automation opportunities that streamline manual implementation processes end to end.
- Use analytical tools and LLMs to process raw customer data and generate updated customer data — including conversion files — for new installations.
- Use AI tools to automate testing of new customer data in preproduction environments ahead of go-live.
- Leverage LLMs to understand and compare like-customer setups across specialties (e.g., AP, Radiology) to accelerate configuration and conversion decisions.
- Build and enhance intelligent testing solutions that integrate with CI/CD pipelines and improve software quality.
- Design, implement, and support AI-assisted developer and implementation tools using technologies such as Claude Code and AWS Bedrock.
- Evaluate emerging AI technologies and prototype new capabilities that improve engineering and implementation productivity.
- Develop scalable services, APIs, and platform components that support Implementations and Engineering teams across the organization.
- Partner with Implementations, Software Engineering, QA, and DevOps to identify opportunities for automation and process improvement.
- Monitor solution performance and continuously optimize AI systems for reliability, accuracy, and scalability.
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:
- Have a builder's mindset with a passion for experimenting, rapidly prototyping, and turning innovative AI concepts into scalable production solutions.
- Bring a process-improvement mindset — comfortable mapping manual implementation workflows and identifying where automation delivers the most value.
- Have strong technical judgment to evaluate emerging technologies, prioritize high-impact opportunities, and make practical, data-driven decisions.
- Can influence through expertise, partnering effectively across Implementations, Engineering, QA, and DevOps to drive adoption of new tools and practices.
- Bring a continuous learning mentality, staying at the forefront of AI-assisted software development and applying new capabilities to improve implementation and engineering productivity.
Skills and experience you have:
You don’t need to check every box. We will consider a combination of education and experience, including:
- Bachelor's degree in Computer Science, Software Engineering, or a related field.
- Minimum of 4 years of AI Development experience
- Strong Python development experience; additional coding language experience preferred.
- Experience building applications using Large Language Models (LLMs), generative AI, or agentic AI workflows.
- Experience with AI platforms such as AWS Bedrock, Anthropic Claude, OpenAI, or similar technologies.
- Experience designing and consuming REST APIs, distributed systems, and cloud-native applications on AWS
- Experience with Git, CI/CD pipelines, Docker, Kubernetes, and modern DevOps practices
- Experience with automated testing frameworks such as Playwright or similar tools
- Understanding of prompt engineering, LLM evaluation, regression testing, and AI system monitoring
- Experience developing internal engineering tools or developer platforms
- Experience with RAG, vector databases, or AI knowledge management technologies is preferred
- Experience working with customer data conversion, data migration, or implementation/onboarding tooling is a plus.
- Healthcare, life sciences, or other regulated industry experience is beneficial but not required. 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: $120,000–$165,000, depending on experience, skills, and level. Candidates may be hired as either an AI Developer or Sr. AI Developer based on qualifications and demonstrated experience.
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.
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 858-436-2901.
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 $120K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $120K to $165K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $165K - $165K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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