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About This Role
Data Integration Analyst, AI & Risk Adjustment
Remote Opportunity
About Reveleer
Reveleer delivers a unified platform spanning risk adjustment, quality improvement, clinical intelligence, and member management for health plans and provider organizations navigating the complexity of value-based care. Trusted by 80+ customer organizations nationwide, the platform integrates data, analytics, and intelligent workflow automation into one governed system designed to support traceable documentation across diagnoses, quality measures, and submissions. With regulatory expertise and transparent, human-in-the-loop AI at its core, Reveleer supports organizations working to advance care quality, strengthen documentation integrity, and sustain the operational readiness needed to navigate audits with confidence.
Why This Role Is Important
The healthcare ecosystem generates an enormous volume of raw data, and most of it is messy, inconsistent, and hard to use. Reveleer's advantage comes from turning that raw material into structured, trustworthy datasets that our platform, our analytics, and our AI can actually leverage. This role sits at the center of that effort.
As a Data Integration Analyst on the AI & Data team, you will design and build the systems and data pipelines that move raw healthcare data into structured, reusable form. You will have ownership of ensuring that customer are able to effectively connect their data systems with the Reveleer ecosystem. Your work will span several connected domains: ingesting and transforming customer data for medical record retrieval; building the data foundations for retrospective risk adjustment coding; developing condition-suspecting algorithms that surface likely undocumented diagnoses; and semantically structuring underlying data so it is far more accessible to AI tools. In short, you turn the raw data of the healthcare ecosystem into the structured assets that everything downstream depends on. You will report to the VP of Data Strategy.
This is a high-autonomy role with a high ceiling. If you are smart, curious, self-motivated, and driven, you will have significant freedom to decide how the work gets done, and a correspondingly large opportunity for impact, growth, and advancement. We are building a team of exceptional people and strive to create an environment where high-performers can grow and excel.
What You'll Do
Data Ingestion & Integration
- Serve as the technical point of contact for receiving customer data (health plans) in whatever format they already use, member, provider, clinic location, chart-retrieval detail, claims, and other source data, rather than requiring them to conform to ours.
- Ingest source data as-is into our SQL Server data warehouse, preserving raw data for traceability, and build scalable, repeatable ingestion routines that handle varied and often messy structures and file types.
- Write and maintain SQL to transform raw source data into accurate, platform-ready outputs, including load files for the Reveleer medical record retrieval platform.
- Monitor pipelines for errors, anomalies, and data quality issues, and remediate them before they affect downstream work.
Retrospective Risk Adjustment & Condition Suspecting
- Build and maintain the data foundations that support retrospective risk adjustment coding workflows.
- Develop, test, and refine condition-suspecting algorithms that surface likely undocumented or under-documented diagnoses from the underlying data.
- Partner with coding, clinical, and analytics teams to translate domain logic into reliable, production-grade data products.
Semantic Structuring for AI
- Design and build semantic data structures that make our underlying healthcare data materially more accessible and useful to AI tools.
- As an early priority, take ownership of a significant library of semantic analytics tables currently maintained in our development environment: refactor and clean up the code, migrate it into production, and stand up automated, scheduled refreshes.
- Establish durable, repeatable standards for promoting code from development to production and for turning massive, raw healthcare data into well-structured, reusable datasets that downstream analytics and AI can leverage.
Quality, Documentation & Collaboration
- Document data models, transformation logic, algorithms, and source-specific rules to build institutional knowledge and reduce rework.
- Partner closely with the Data Management, analytics, and platform teams to ensure smooth handoffs and dependable production systems.
- Work with the VP of Data Strategy and cross-functional partners to continuously improve how raw healthcare data becomes structured, AI-ready assets.
Who Thrives in Role
We are looking for exceptionally smart, highly motivated people with a very high ceiling, the kind who are energized by ambiguity, take ownership without being asked, and consistently find a better way. If that is you, this role offers unusual freedom to operate and real room to grow.
- You bring rigorous analytical and problem-solving skills, ideally developed in a quantitative or scientific discipline. We especially welcome candidates transitioning from academic or research careers, for example, graduate study or research experience in fields such as molecular biology, genetics, physics, chemistry, or other quantitative sciences, who want to apply their talents in a fast-moving, high-impact commercial environment.
- You have demonstrated aptitude in designing data transformation systems that generate meaningful real-world outcomes. You have hands-on, demonstrable experience with database technology; the specific platform matters far less to us than your ability to learn quickly, build, and show real working competence.
- You are driven by autonomy and results. Given freedom, you run with it, and you want to be somewhere that recognizes and rewards that.
Qualifications
Required
- Bachelor's degree in a quantitative, scientific, technical, or related field, or equivalent hands-on experience. Advanced degrees and research backgrounds in quantitative or scientific fields are welcome.
- Proven, hands-on coding ability and demonstrable experience working with database technology. We work primarily in SQL Server / T-SQL, but strong experience in any comparable relational database transfers well, the specific platform matters far less than your ability to code and to prove it.
- Demonstrated ability to design creative, original solutions to parse, structure, and make sense of complex, messy, or unfamiliar data.
- Comfort designing, testing, and refining algorithms or analytical logic against real-world data.
- Experience cleaning, mapping, and validating real-world data from multiple sources and file formats into a required structure.
- Experience building repeatable, maintainable ETL or data-ingestion workflows.
- Strong attention to detail and a structured, methodical approach to data quality and validation.
- Clear written and verbal communication skills, including the ability to work directly with customers and internal teams.
- Self-starter who takes ownership end-to-end and thrives with autonomy.
Preferred
- Experience in health insurance or another healthcare-related field is ideal but not required, strong candidates without healthcare experience are encouraged to apply.
- Graduate study or research experience in a quantitative or scientific discipline.
- Familiarity with SQL Server Integration Services (SSIS) or comparable ETL tooling.
- Exposure to Python for data manipulation and automation.
Understanding risk adjustment (including retrospective risk adjustment coding, HCCs, or condition suspecting), medical record retrieval, or value-based care operations.
- Exposure to preparing, structuring, or engineering data for AI/ML tools and workflows.
- Experience supporting customer onboarding or client data implementations.
WHAT YOU'LL RECEIVE:
- Competitive salary
- Medical, Dental and Vision benefits
- 401k match
- Generous PTO plan
Our compensation reflects the cost of labor across several US geographic markets. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.
Reveleer E-Verifies all new hires.
Reveleer is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, disability status or genetic information, in compliance with applicable federal, state and local law.
Salary Context
This $105K-$135K range is in the lower quartile 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 reveleer, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $105K to $135K.
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.
reveleer AI Hiring
reveleer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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