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About This Role
About Curve Dental:
Build the Intelligence Layer for Modern Dentistry
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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 Platform Software Developer to join our team. Working alongside a highly experienced architecture team, you’ll bring deep expertise in agentic AI, machine learning, and intelligent systems while helping shape how AI is applied across the Curve platform.
This is a hands\-on development role where you’ll collaborate on architectural decisions, build production AI capabilities, and help establish the development patterns and practices that enable intelligent applications across our products.
We move quickly, collaborate closely, and take ownership. Whether you’re designing a new platform capability, reviewing code, troubleshooting a production issue, or helping another team 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 foundation that will power the next generation of dental software.
What You’ll Do
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As an AI Platform Software Developer, you’ll work alongside our Architecture, Development and Product teams to help define and deliver the next generation of intelligent capabilities across the Curve platform.
This is a hands\-on engineering role. You’ll spend the majority of your time designing, building, reviewing, and delivering production software while helping guide the technical direction of AI across the organization.
Your responsibilities will include:
- Partnering with the Architecture team to evolve Curve’s AI platform and establish development standards, reusable frameworks, and development patterns that enable teams to build intelligent applications at scale.
- Collaborating on the technical design, development, testing, and delivery of production services, agentic workflows, MCP servers, machine learning capabilities, and shared platform components.
- Writing high\-quality production code while leveraging modern AI\-assisted development tools, including Claude Code, to improve engineering productivity and software quality.
- Working side\-by\-side with developers, product managers, and UX designers to transform business requirements into secure, scalable, maintainable production software
- Performing technical design reviews, performing code reviews, mentoring developers, and contributing hands\-on throughout the entire software development lifecycle
- Solving complex development challenges involving distributed systems, intelligent workflows, APIs, cloud\-native services, and modern AI technologies
- Continuously evaluating emerging AI technologies, frameworks, and development practices, bringing practical ideas that improve both our products and the way we build them
- Partnering with internal development teams and external partners to build, extend, and continuously evolve Curve’s AI platform
- Taking ownership of production software by helping troubleshoot complex issues, improving reliability, and ensuring our AI capabilities meet the high standards our customers expect
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
- 8\+ years of professional software development experience building scalable SaaS applications
- 3\+ years designing, building, and deploying production AI applications, platforms, or intelligent workflow solutions
- Demonstrated experience taking AI 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 delivering software that customers rely on every day
- Strong expertise in modern backend development, with experience in Python or similar technologies
- Experience building or extending agentic AI systems, MCP servers or similar integration frameworks, machine learning solutions, and LLM based applications
- Strong understanding of distributed systems, APIs, cloud\-native architectures and modern software development practices
- Experience participating in technical architecture discussions and collaborating across development teams to solve complex technical challenges
- Passion for mentoring developers through technical design, code reviews, pair programming, and hands\-on collaboration
- Experience supporting and operating production software, including troubleshooting complex issues and driving problems through resolution
- Experience using AI\-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or similar technologies with a desire to continually improve productivity and quality
- Thrives in a fast\-paced environment, embraces ownership, and enjoys tackling difficult technical challenges wherever they arise
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
- Retrieval\-Augmented Generation (RAG), vector databases, and semantic search
- Production AI operations, including model deployment, evaluation, observability, governance, or MLOps
- Conversational AI, speech technologies or customer facing AI applications
- 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:
- Become a trusted member of Curve’s development team and a technical resource for teams across the organization
- Help shape the technical evolution of our AI platform while contributing to architectural decisions and development standards
- Design, build, and deliver production AI capabilities that are adopted across the Curve platform
- Establish reusable frameworks, development patterns, and best practices that accelerate AI development across multiple product teams
- Help developers leverage AI technologies and modern development tools to improve both software quality, development productivity, and delivery speed
- Become the developer others seek out when tackling the organization's most challenging AI and platform development problems
- Contribute to building a platform that enables intelligent workflows across clinical, operational, financial, and patient\-facing experiences
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Curve Dental, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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