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Company: DentaSmart
Employment Type: Full\-time Executive
Location: United States — Remote
Travel: Regular U.S. travel required
Reports To: Founder and CEO
About DentaSmart
DentaSmart is building a modern Dental Benefits Solution that combines:
Personal AI Dentist \+ Discounted Dental Network \+ Financing
DentaSmart helps individuals and families understand, monitor, and manage their oral health through AI\-powered mouth\-image and dental X\-ray analysis, Oral Health Scores, Second Opinions, personalized reports, treatment considerations, cost guidance, family profiles, and 24/7 oral\-health support.
Through its discounted dental network, members can access participating dentists and save on dental care. Financing options will help make treatment costs more manageable.
DentaSmart can be offered as a standalone dental benefit, an alternative for populations without dental insurance, or a complement to existing dental coverage.
The Role
DentaSmart is seeking an experienced Chief Revenue Officer with extensive dental insurance sales and distribution experience to build revenue from the ground up.
This is a hands\-on executive role. The CRO will personally open doors, develop relationships, create the sales strategy, secure initial customers, negotiate agreements, and build the revenue team as the company grows.
The ideal candidate has strong relationships across the dental insurance, employee benefits, employer, broker, association, union, and self\-insured\-plan markets.
Primary ResponsibilitiesSell DentaSmart to Insurance and Self\-Insured Plans
Develop and close opportunities with:
- Dental insurance carriers
- Health insurance companies
- Self\-insured and self\-funded employers
- Third\-party administrators
- Dental plan administrators
- Benefits and population\-health platforms
Position DentaSmart as a solution for:
- Oral\-health monitoring and prevention
- Member engagement
- AI\-powered dental assessments
- Second Opinions
- Treatment\-plan clarity
- Increased preventive dental visits
- Cost containment
- Avoidable emergency\-room utilization reduction
- Fraud, waste, and abuse support
- Member retention and plan differentiation
Sell the DentaSmart Dental Benefits Solution
Build distribution through:
- Employers and employer groups
- Small, midsize, and large businesses
- Professional and trade associations
- Labor unions
- Membership and affinity organizations
- Franchise groups
- Gig\-economy platforms
- Staffing companies
- Benefits brokers and consultants
- General agencies
- Voluntary\-benefits platforms
- Professional employer organizations
- HR, payroll, and benefits technology companies
Present the DentaSmart Dental Benefits Solution consistently as:
Personal AI Dentist \+ Discounted Dental Network \+ Financing
The CRO will determine the best distribution model, including employer\-paid, voluntary, association\-sponsored, union\-sponsored, broker\-distributed, and embedded\-benefit programs.
Build the Revenue Function
- Create DentaSmart’s enterprise and group\-sales strategy.
- Define target markets, pricing, commissions, channel economics, and sales processes.
- Build a qualified pipeline from zero.
- Personally lead major presentations, proposals, negotiations, and closings.
- Secure pilot programs, group agreements, carrier partnerships, and distribution contracts.
- Develop relationships with brokers, consultants, general agencies, and strategic channel partners.
- Establish CRM, forecasting, pipeline\-management, and reporting processes.
- Create repeatable sales playbooks and customer\-segmentation strategies.
- Recruit and lead the future sales, partnerships, account\-management, and revenue\-operations teams.
- Work with product, technology, operations, clinical, finance, and compliance teams to support successful implementations.
- Develop renewal, expansion, and member\-enrollment strategies.
- Provide accurate revenue forecasts and regular updates to the CEO and Board.
First\-Year Objectives
- Build DentaSmart’s commercial strategy and sales infrastructure.
- Create a strong pipeline of insurers, self\-insured plans, employers, associations, unions, brokers, and distribution partners.
- Activate existing dental\-industry relationships.
- Secure initial enterprise pilots and paying customers.
- Sign employer\-group and distribution agreements.
- Develop broker and channel\-partner programs.
- Establish pricing, commission structures, proposals, sales materials, and implementation processes.
- Build a clear path toward scalable and predictable revenue.
- Hire the initial revenue team as commercial traction grows.
Required Experience
- Extensive experience in dental insurance, dental benefits, employee benefits, or a related payer market.
- Demonstrated success selling dental insurance, dental plans, dental networks, voluntary benefits, or related solutions.
- Experience selling to both:
- Insurance carriers, self\-insured plans, third\-party administrators, or payer organizations; and
- Employers, associations, unions, brokers, consultants, or employer groups.
- Strong existing relationships within the dental insurance and employee\-benefits industries.
- Proven ability to personally develop and close complex enterprise opportunities.
- Experience working with benefits brokers, general agencies, consultants, and distribution partners.
- Understanding of dental plan design, dental networks, group benefits, voluntary benefits, and employer\-sponsored coverage.
- Experience building a sales organization, business line, or revenue function from an early stage.
- Strong negotiation, forecasting, pipeline\-management, and executive\-presentation skills.
- Willingness to operate as a hands\-on seller before building a larger team.
- Authorization to work in the United States.
Preferred Experience
- Leadership experience at a dental insurance carrier, dental benefits company, brokerage, benefits platform, or dental network.
- Experience with self\-insured employers, association benefits, union benefits, voluntary benefits, or affinity programs.
- Experience selling digital\-health, payer\-technology, member\-engagement, cost\-containment, or fraud, waste, and abuse solutions.
- Experience launching a new dental product, benefits program, or distribution channel.
Ideal Candidate
The ideal candidate is a respected dental\-benefits executive who knows how dental products are sold, implemented, enrolled, renewed, and expanded.
This person must be strategic but highly hands\-on, have the relationships to secure senior\-level meetings, and be comfortable building a commercial organization from the beginning.
The successful candidate will clearly communicate how DentaSmart complements traditional dental coverage while creating an affordable solution for individuals, families, employers, and groups.
Compensation
DentaSmart will offer a competitive executive compensation package that may include:
- Base salary
- Performance\-based commission or bonus
- Equity participation
- Company benefits
- Business travel reimbursement
Compensation will be based on the candidate’s experience, industry relationships, and record of building revenue.
How to Apply
Please submit a résumé and brief cover letter describing:
- Your dental insurance and dental\-benefits sales experience
- The carriers, employers, brokers, associations, unions, or payer organizations you have sold to
- Examples of major contracts or distribution partnerships you personally developed
- Your experience building a revenue function or launching a new product
- What you would prioritize during your first 90 days at DentaSmart
DentaSmart is an equal\-opportunity employer.
Pay: $69,560\.81 \- $123,772\.16 per year
Work Location: Remote
Salary Context
This $69K-$123K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Zigron Inc, 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($96K) sits 55% below the category median. Disclosed range: $69K to $123K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Zigron Inc AI Hiring
Zigron Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $123K - $123K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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