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About This Role
##### About Planet DDS
We’re on a mission to fix dental software \- and we’re not playing small. Our platform replaces clunky, outdated systems with modern, cloud\-based, AI\-powered technology built to actually work at scale. From practice management to imaging to revenue cycle automation, we’re tearing down the old infrastructure and rebuilding the future of dentistry. Planet DDS is the fastest\-growing provider of dental practice management solutions and the \#1 cloud platform for DSOs and multi\-location groups.
Here, you won’t just join a team \- you’ll join a movement. We want bold thinkers who are ambitious enough to push limits, empathetic enough to work as one, and accountable enough to own big outcomes. Trust is our currency, collaboration is our edge, and impact is our fuel. If you’re ready to grow fast, challenge the status quo, and help reinvent an entire industry, Planet DDS is where you belong.
To learn more, visit: Planet DDS.
Overview
We are seeking an AI\-Powered QA Engineer III, to test SaaS and mobile products for dental offices and dental groups. This individual will leverage AI tools and capabilities to enhance and expedite their software testing workflow. To be successful, the engineer will need to be self\-motivated, a critical thinker, be able to take high\-level directions, communicate clearly, and drive to completion in a very fast\-paced environment.
##### Job Duties
- Bring AI tools and capabilities advocacy to an engineering team
- Design, implement and maintain the automated testing framework for SaaS products.
- Mentor and train other team members on automation, quality practices and principles.
- Contribute to code reviews, design reviews, effort estimates, task breakdowns, and other team discussions.
- Assess and raise risks across the Planet DDS solution.
- Work with the engineering team continuously improving the performance, scalability, and reliability of Planet DDS products.
- Collaborate with the product and engineering teams to design and deploy new features.
- Learn about the latest tools and patterns consistent with your role.
- Other duties as assigned.
Skills and Qualifications
- 5\-7 or more years of experience in software engineering development, test, and/or QA role
- Track record using AI\-powered software testing tools like Applitools, Testim, Mabl, TestRigor, TestComplete, UI\-TARS, Midscene and Puppeteer
- Experience in automation testing and building automation frameworks from the ground up
- Experience in programming (ideally JavaScript/TypeScript and/or C\#)
- Experience with: Test case management techniques, technologies, and approaches.
- Experience with Jira \& Zephyr Scale would be beneficial
- Knowledge of how to characterize different risk scenarios (Examples: functional/closed\-box, boundary, verification and validation, acceptance, load, stress, performance, HA/DR)
- Sense of ownership and pride in your performance and its impact on the company’s success
- Strong collaboration and communication skills
- Ability to engage throughout the department and the company to achieve goals
Benefits:
- Medical, dental and vision insurance
- Health Savings Account
- Flexible Spending Accounts
- Telehealth
- 401(k) and 401(k) match
- Life and AD\&D insurance
- Short\-Term and Long\-Term Disability
- FTO or Vacation
- Sick Time
- Employee Well\-Being program
- 11 paid holidays
- Volunteer Time Off
- Employee Referral program
- Additional perk and voluntary benefit programs
Salary is based on a number of factors and may vary depending on job\-related knowledge, skills, and experience. This position is also eligible for variable pay as part of the total compensation package.
PLANET DDS CORE IDEOLOGY:
To encourage measurable progress toward our vision and make the best decisions on behalf of employees and customers, we adopted a set of common values:
- Collaborative – Working independently and across teams, we create scalable solutions to enable company growth
- Empathetic – We are educated on the experience of our customers and feel vested in their success
- Accountable – We feel ownership for the quality of our work and take pride in the positive outcomes
- Trustworthy – We operate with integrity and honest, making promises we know that we can keep
- Ambitious – We are driven by our ability to make a long\-term, positive impact on the lives of dental market leaders
Planet DDS is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by applicable law.
Salary Context
This $83K-$114K 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 Planet DDS, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($98K) sits 54% below the category median. Disclosed range: $83K to $114K.
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.
Planet DDS AI Hiring
Planet DDS has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $114K - $135K.
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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