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About This Role
About Us:
Ventra is a leading business solutions provider for facility\-based physicians practicing anesthesia, emergency medicine, hospital medicine, pathology, and radiology. Focused on Revenue Cycle Management, Ventra partners with private practices, hospitals, health systems, and ambulatory surgery centers to deliver transparent and data\-driven solutions that solve the most complex revenue and reimbursement issues, enabling clinicians to focus on providing outstanding care to their patients and communities.* Come Join Our Team!
+ As part of our robust Rewards \& Recognition program, this role is eligible for our Ventra performance\-based incentive plan, because we believe great work deserves great rewards.
- Help Us Grow Our Dream Team — Join Us, Refer a Friend, and Earn a Referral Bonus!
Job Summary:
- The Manager, Provider Enrollment is primarily responsible for overseeing the Provider Enrollment Department.
Essential Functions and Tasks:
- Manages Provider Enrollment Department operations; recruits, selects, orients, trains, coaches, counsels, and disciplines staff. Plans, monitors, appraises, and reviews staff job contributions. Implements cross functional training and succession planning as allowable.
- Develops relationship with clients and provides regular status updates on the credentialing process on a regular basis.
- Coordinates with client leadership and credentialing staff as necessary to obtain documents and additional information necessary to complete enrollment process.
- Holds monthly (or as warranted) meetings with all levels of management to review held claims and address concerns.
- Reports major issues to MGA leadership and makes aware of potential threats/opportunities.
- Develops policies, guidelines, and implements procedures and ensures consistent department\-wide implementation. Enhances and standardizes work\-flow processes throughout the enrollment cycle to assist in achieving consistency and success.
- Monitors timeliness and effectiveness of department activities.
- Compiles and prepares a variety of reports for management in order to analyze trends and make recommendations.
- Conducts regular meetings with the team and provides suggestions for improvement and decision support
- Protects and safeguards company and patient information and reports suspicious activity to management.
- Performs special projects and other duties as assigned.
Education and Experience Requirements:
- High School diploma or equivalent.
- Bachelor’s Degree in Healthcare Administration, Business Administration, Benefits, or equivalent training and/or experience preferred.
- At least two (2\) years of physician billing, hospital billing, or vendor management experience in provider enrollment functions.
- At least one (1\) year of supervisory/management/leadership experience preferred.
- Experience with CAQH database, NPI website and maintaining EDI, EFT, and ERA processes preferred.
Knowledge, Skills, and Abilities:
- Knowledge of business and financial processes, procedures, and processes.
- Knowledge of medical terminology and anatomy.
- Knowledge of requirements of medical record documentation.
- Strong supervisory/management skills.
- Strong management level oral, written, and interpersonal communication skills.
- Strong financial reporting skills.
- Strong healthcare data analysis skills.
- Strong presentation development and delivery skills.
- Strong word processing, spreadsheet, database, and presentation software skills.
- Strong decision\-making skills.
Compensation:
- Base Compensation will be based on various factors unique to each candidate including geographic location, skill set, experience, qualifications, and other job\-related reasons.
- This position is also eligible for a discretionary incentive bonus in accordance with company policies.
Ventra Health:
Equal Employment Opportunity (Applicable only in the US)
Ventra Health is an equal opportunity employer committed to fostering a culturally diverse organization. We strive for inclusiveness and a workplace where mutual respect is paramount. We encourage applications from a diverse pool of candidates, and all qualified applicants will receive consideration for employment without regard to race, color, ethnicity, religion, sex, age, national origin, disability, sexual orientation, gender identity and expression, or veteran status. We will provide reasonable accommodations to qualified individuals with disabilities, as needed, to assist them in performing essential job functions.
Recruitment Agencies
Ventra Health does not accept unsolicited agency resumes. Ventra Health is not responsible for any fees related to unsolicited resumes.
Solicitation of Payment
Ventra Health does not solicit payment from our applicants and candidates for consideration or placement. Attention Candidates
Please be aware that there have been reports of individuals falsely claiming to represent Ventra Health or one of our affiliated entities Ventra Health Private Limited and Ventra Health Global Services. These scammers may attempt to conduct fake interviews, solicit personal information, and, in some cases, have sent fraudulent offer letters.
To protect yourself, verify any communication you receive by contacting us directly through our official channels. If you have any doubts, please contact us at Careers@VentraHealth.com to confirm the legitimacy of the offer and the person who contacted you. All legitimate roles are posted on https://ventrahealth.com/careers/. Statement of Accessibility
Ventra Health is committed to making our digital experiences accessible to all users, regardless of ability or assistive technology preferences. We continually work to enhance the user experience through ongoing improvements and adherence to accessibility standards. Please review at https://ventrahealth.com/statement\-of\-accessibility/.
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 VENTRA Health, 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.
VENTRA Health AI Hiring
VENTRA Health has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Dallas, TX, US.
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
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