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About This Role
About Curve Dental:
Build the Next Generation of AI\-Enabled Dental Software
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Curve Dental is the leading cloud\-based dental practice management platform, helping thousands of dental practices streamline operations, improve patient experiences, and grow their businesses.
We’re entering the next phase of the Curve platform. We believe the future of dental software isn’t simply AI\-powered, it is AI\-enabled. Curve is building a shared intelligence platform that brings agentic AI into every aspect of the dental practice. From revenue cycle management and patient communications to scheduling, clinical documentation, insurance workflows, and operational automation, our goal is to create intelligent systems that help practices operate more efficiently while allowing dental teams to focus on delivering exceptional patient care.
To help bring this vision to life, we’re looking for an AI Software Developer to join our team. Working alongside our AI Platform Software Developer, Architecture team, and highly skilled development teams, you’ll help design, build, and deliver production AI capabilities that power intelligent workflows throughout the Curve platform.
This is a hands\-on development role. You’ll spend the majority of your time, designing, building, testing, reviewing, and delivering production software while helping bring intelligent agents, machine learning models, and AI\-powered workflows into the hands of thousands of customers.
We move quickly, collaborate closely, and take ownership. Whether you’re building a new intelligent workflow, deploying a machine learning model, troubleshooting a production issue, or helping another developer solve a difficult technical challenge, you’ll be expected to jump in where needed and help deliver the best possible product.
This is an opportunity to help build the AI capabilities that will power the next generation of dental software.
What You’ll Do
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As an AI Software Developer, you’ll work alongside our Architecture, Development and Product teams to build and deliver intelligent capabilities across the Curve platform.
Your responsibilities will include:
- Designing, building, testing, and delivering production AI capabilities, including intelligent agents, workflows, MCP servers, integrations, machine learning models, and shared services
- Developing AI\-powered products that automate clinical, operational, financial, and patient\-facing workflows across the Curve platform
- Building, training, evaluating, deploying, monitoring, and continuously improving machine learning models and intelligent services running in production
- Developing reusable MCP tools, APIs, and platform components that accelerate AI development across multiple platform teams
- Writing high\-quality production code while leveraging modern AI\-assisted development tools, including Claude Code, to improve software quality and development productivity
- Participating in technical design discussions, code reviews, testing, production deployments, and production support throughout the software development lifecycle
- Working closely with Product, UX, Architecture, and development teams to transform business requirements into secure, scalable, maintainable software
- Continuously improving AI capabilities through experimentation, prompt engineering, model tuning, evaluation, monitoring, and operational feedback
- Taking ownership of production software by troubleshooting complex issues, improving reliability, and helping deliver exceptional customer experiences
What We’re Looking For
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We’re looking for an exceptional software developer who combines deep hands\-on AI expertise with a passion for building production systems.
Required Qualifications
- 5\+ years of professional software development experience building scalable SaaS applications
- 2\+ years designing, building, and deploying production AI applications, platforms, or intelligent workflow solutions
- Demonstrated experience taking AI\-powered solutions from proof\-of\-concept through production deployment and continuously improving them through evaluation, monitoring, iteration and operational feedback
- Demonstrated experience writing high\-quality production code and supporting production software
- Strong expertise in modern backend development, with experience in Python or similar technologies
- Experience building intelligent agents, MCP servers, AI tools, or similar integration architectures
- Experience training, deploying, evaluating, and maintaining machine learning models in production
- Experience working with LLMs, embeddings, Retrieval\-Augmented Generation (RAG), vector databases, and prompt engineering
- Strong understanding of distributed systems, APIs, cloud\-native architectures, and modern software development practices
- Experience participating in technical design discussions, code reviews, and collaborative software development
- Experience using AI\-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or similar technologies
- Thrives in a fast\-paced environment, embraces ownership, and enjoys solving difficult development challenges
Preferred Qualifications
Experience with one or more of the following:
- Production AI platforms (LangGraph, Amazon Bedrock, OpenAI, Anthropic, or similar technologies)
- Agentic AI frameworks and MCP servers, and AI integration ecosystems
- Production AI operations, including model deployment, evaluation, observability, governance, or MLOps
- Conversational AI, speech technologies or customer facing AI applications
- Retrieval\-Augmented Generation (RAG), vector databases, and semantic search
- Building AI capabilities within large\-scale, multi\-tenant SaaS platforms
- Experience building software in healthcare or other highly regulated environments
What Success Looks Like
During your first year, you will:
- Deliver production AI capabilities that are adopted across the Curve platform and used by thousands of dental practices
- Become a trusted technical contributor on AI initiatives across multiple product teams
- Build reusable agents, tools, workflows, and platform components that accelerate AI development throughout the organization
- Help evolve Curve’s AI platform through practical implementation, continuous improvement, and production feedback
- Consistently deliver high\-quality production software while helping improve development practices across the development organization
- Become a developer that others rely on when solving difficult AI development challenges and building reliable production systems
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Curve Dental, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Curve Dental AI Hiring
Curve Dental has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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