Auditor Technical Trainer - DRG

$105K - $125K Remote Mid Level AI/ML Engineer

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Skills & Technologies

Rag

About This Role

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Overview:

This role is part of the training team for the Clinical Chart Validation team. This position is responsible for improving the technical effectiveness of our teams by planning, developing, and delivering technical training, mentoring, and assessment. The individual will work collaboratively with subject matter experts in the Commercial \& Government Audit Teams, Quality Assurance, Concept Development, and others to validate workflows and communication tools to enhance audit productivity, performance, and client satisfaction.

Responsibilities:

Training, Development, and Mentoring.* Assess job\-specific needs and develop technical training plans with clear business objectives, including working with subject matter experts, developing training materials, and developing appropriate assessments and measurements of success.

  • Select training/instructional methods and procedures appropriate for the situation when learning or teaching new skills.
  • Deliver specific training sessions, including using suitable delivery methods such as classroom, online, and webinar. Identify the development needs of others and coach, mentor, or otherwise assist others with improving their knowledge skills.
  • Provide support to the CCV audit team members; assist with orientation of new members as needed, mentor new team members after orientation.
  • Promote audit accuracy measures by training/educating and mentoring the auditor and providing documented and validated findings.
  • Encourage critical thinking and discussion among team members on concepts as needed.
  • Provide training on one or more of the following audit types: Outpatient and Specialty Review Types to include SNF, IRF, and HH.
  • Train Clinicians with coding certifications on coding principles.
  • Confer with management and conduct surveys to identify training needs based on projected production processes, changes, and other factors.
  • Participate in weekly/monthly team meetings to share best practices initiatives and recommend audit vulnerabilities.
  • Support the Medical Director to ensure accurate assessments of improper payments are based on consistent application of clinical guidelines.

Assess customer/provider/stakeholder issues, complaints, and compliments.* Monitor/Assess the performance of self, other individuals, \&/or organizations to make improvements or recommend remediation or corrective action.

  • Work with the Quality Team to train audit team members on findings from quality review audits.
  • Develop testing and evaluation procedures. Evaluate instructor performance and the effectiveness of training programs, providing recommendations for improvements. Conduct or arrange for ongoing technical training and personal development classes for staff members.

Quality Assurance Controls.* Integrate healthcare auditing principles and use objectivity in the performance of medical audit activities and reviews.

  • Draw on healthcare proficiency and industry knowledge to substantiate conclusions.
  • Perform work independently, review and interpret audit work of others.
  • Depending on the nature and scope of the audit, may review medical records and apply in\-depth knowledge of clinical criteria to determine medical necessity, appropriateness of setting, potential billing/coding issues, and quality concerns.
  • Demonstrate an understanding of complex contract specifications when performing medical record reviews.
  • Use healthcare expertise to determine approval or referral to the Medical Director.
  • Provide feedback on reviews to the Quality Assurance Manager as indicated in order to assist with the improvement of rationales sent to providers.

New Concepts and Processes.* Develop reasonable and effective recommendations for concept solutions that reflect an understanding of the client environment and risks inherent to our business and industry.

  • Suggest and or develop and implement new ideas, approaches, decision trees, and/or technological improvements that will support and optimize audit results.
  • Collaborate with Data Services in developing new reports.

Meets or Exceeds Standards/Guidelines for Productivity.* In addition to regular and predictable attendance, maintain production goals and quality standards set by the audit.

  • Performs QA audits against the expected level of quality and quantity (i.e. hit rate, \# claims written, ID per hour).

This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.

Qualifications:

  • Associates Degree or equivalent relevant experience required. Bachelor’s degree in Nursing, Healthcare Economics, Health Information Management, and/or Business, preferred, or 5 – 7 years of relevant experience (experience in any of the following: claims auditing/quality assurance/recovery auditing).
  • Clinical /Nursing experience in an SNF, IRF, and HH setting is required.
  • Coding certification is required and maintained as a condition of employment. (CCS, CPC, etc.). Candidates who hold a CCDS will also be given consideration but will need to obtain a coding certification within 6 months.
  • 5 to 7\+ years of working with a broad knowledge of medical claims, billing/payment systems provider billing guidelines, payer reimbursement policies, medical necessity criteria, and coding terminology.
  • Adherence to official coding guidelines, coding clinic determinations, and CMS and other regulatory compliance guidelines and mandates. Requires expert coding knowledge \- CPT and HCPCS codes.
  • Strong presentation skills. Comfortable in presenting/defending audit logic to clients and key stakeholders (i.e. hospitals, physicians, validation contractors, auditing team, etc.).
  • Independent thinker, logical, strategic, with a high focus and attention to detail.
  • Effective communication and presentation style (written and verbal) with proven ability to positively influence behavior and outcomes.
  • Knowledge of principles and methods for curriculum and training design, teaching and instruction for individuals and groups, and the measurement of training effects.
  • Competent administrative and organizational skills, ability to multitask, set priorities, and meet deadlines.
  • Professional demeanor: Ability to creatively solve problems, deal with ambiguity, develop and implement policy and procedures, perform analysis and prepare reports, and foster team building.
  • High level of proficiency with all audit technology i.e., R3, CAT, etc.
  • Proficiency in Word, Access, Excel, PowerPoint and other applications.
  • Excellent written and verbal communication skills.
  • Applicants should have home health, IRF, and SNF experience.

Mental Requirements:* Communicating with others to exchange information.

  • Problem\-solving and thinking critically.
  • Completing tasks independently.

Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.

  • Repeating motions that may include the wrists, hands, and/or fingers.
  • Must be able to provide a dedicated, secure work area.
  • Must be able to provide high\-speed internet access/connectivity and office setup and maintenance.
  • No adverse environmental conditions are expected.

Base compensation ranges from $105,000 to $125,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This role is eligible for discretionary bonus consideration.

Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page.

Date of posting: 4/8/2026

Applications are assessed on a rolling basis. We anticipate that the application window will close on 6/8/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.

\#LI\-Remote

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Salary Context

This $105K-$125K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Company Cotiviti
Title Auditor Technical Trainer - DRG
Location Remote, US
Category AI/ML Engineer
Experience Mid Level
Salary $105K - $125K
Remote Yes

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 Cotiviti, 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

Rag (64% of roles)

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. This role's midpoint ($115K) sits 31% below the category median. Disclosed range: $105K to $125K.

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.

Cotiviti AI Hiring

Cotiviti has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety, AI Software Engineer. Based in Remote, US. Compensation range: $124K - $180K.

Remote Work Context

Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 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

Based on 13,781 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $166,983. Actual compensation varies by seniority, location, and company stage.
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.
About 7% of the 26,159 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Cotiviti is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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