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About This Role
At Commure, we're building the AI Operating System for healthcare, the foundation that defines how care is delivered, documented, and financed. Our platform spans the full care journey: Ambient AI and Dictation eliminating documentation burden at the point of care, intelligent Agents automating patient and revenue workflows, and autonomous RCM processing billions in claims, all on a single AI\-native platform integrated with 60\+ EHRs.
Healthcare carries a $1 trillion administrative burden and we're at the center of transforming it. Today, 500,000\+ clinicians across 500\+ healthcare organizations nationwide trust Commure to handle $25B\+ in annual claims and support over 200 million patient interactions. Our latest $70M raise at a $7B valuation reflects the confidence the market has placed in this mission. We've also been named to the Fortune Future 50 list and the 2026 AI Breakthrough Awards for “Overall NLP Company of the Year.”
Our team works directly alongside clinicians, not through layers of process, which means the gap between what you build and its impact on patient care is immediate. We move fast, deploy daily, and take full ownership from early thinking to production. If you're energized by hard problems, high stakes, and a team that holds itself to a high bar, you'll find your people here.
The future of healthcare is being built right now. Come deliver this transformation.
About the Role
At Commure, the AI Integration Team plays a crucial role in supporting the company’s core products, Revenue Cycle Management (RCM) and Ambient, by ensuring seamless data flow between our platform and over 40 Electronic Medical Records (EMRs). This team is responsible for fetching vital Encounter and Appointment data from multiple EMRs and, when necessary, writing data back to these systems. The team's work ensures the interoperability and smooth exchange of healthcare data via HL7, enabling healthcare providers to deliver more efficient and accurate care while streamlining administrative processes. We're looking for a Staff Software Engineer who takes ownership end\-to\-end, moves fast, and wants their work to directly impact how care is delivered.
What You'll Do
- Design and implement complex integration solutions between our platform and third\-party healthcare systems (EHRs, insurance providers, etc.) while ensuring high performance, reliability, and scalability.
- Utilize the latest developments in LLM technology to automate the reverse\-engineering of electronic health records.
- Collaborate with cross\-functional teams (product, engineering, and healthcare) to gather integration requirements, define technical approaches, and prioritize tasks based on business needs.
- Contribute to the technical direction of the integration platform by proposing and implementing improvements to architecture, tools, and technologies.
- Take ownership of end\-to\-end integration workflows, from design and development through to deployment and post\-launch monitoring.
- Identify and resolve performance bottlenecks, troubleshoot integration issues, and proactively improve the reliability of integrations in production environments.
- Ensure all integration work adheres to healthcare compliance and security standards (e.g., HIPAA, HL7, FHIR) and collaborate with security teams to protect sensitive data.
- Work closely with junior engineers to provide technical guidance and mentorship, helping them solve problems and ensuring high\-quality code standards are followed.
- Drive the implementation of best practices for integration development, including testing, error handling, version control, and continuous integration.
- Collaborate with DevOps and QA teams to implement monitoring and alerting systems that ensure integration reliability and system health in production.
- Stay up\-to\-date with industry trends in healthcare technology, integration patterns, and emerging tools, continuously suggesting and applying innovations that can improve system performance and integration capabilities.
- Participate in agile development cycles, contributing to sprint planning, retrospectives, and maintaining a collaborative and efficient working environment
What You Have
- \[Required] Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent extensive experience
- 10\+ years of professional software development industry experience
- Proficiency in backend languages like Python, Go, or C\+\+.
- Proficiency in frontend languages like Javascript, HTML, CSS
- Experience including but not limited to Git, GCP / AWS, Docker, Kubernetes, relational and non\-relational DBs, and monitoring/alerting tools
- Proven track record of designing and implementing end\-to\-end solutions and making successful architectural decisions
- Excellent communication and collaboration skills, with the ability to work in a fast\-paced, dynamic environment.
Please be aware that all official communication from us will come exclusively from email addresses ending in @commure.com. Any emails from other domains are not affiliated with our organization.
Employees will act in accordance with the organization’s information security policies, to include but not limited to protecting assets from unauthorized access, disclosure, modification, destruction or interference nor execute particular security processes or activities. Employees will report to the information security office any confirmed or potential events or other risks to the organization. Employees will be required to attest to these requirements upon hire and on an annual basis.
Compensation Range: $170K \- $240K
Salary Context
This $170K-$240K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Commure, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($205K) sits 6% below the category median. Disclosed range: $170K to $240K.
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.
Commure AI Hiring
Commure has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Mountain View, CA, US. Compensation range: $180K - $240K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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