Interested in this AI/ML Engineer role at LRx Healthcare?
Apply Now →About This Role
CTO Co\-Founder – Healthcare AI \& Payments
Location: New York, NY
Work Type: Full\-Time, In\-Person
Industry: Healthcare Technology, Artificial Intelligence, Payments, FinTech
Experience: Senior / Founding\-Level Technical Leadership
Visa Sponsorship: Not Available
Relocation Assistance: Not Available
Job Summary
We are seeking a hands\-on CTO Co\-Founder to help build and scale an early\-stage, funded Healthcare AI and Payments company in New York City.
This is not an advisory or purely executive CTO position. We are looking for a technical builder who can design architecture, write code, launch an MVP, build AI\-enabled workflows, and recruit the early engineering team.
The platform will focus on improving healthcare financial operations using artificial intelligence, workflow automation, secure data infrastructure, claims technology, payment processing, and revenue cycle management solutions.
Key Responsibilities
- Own the technology strategy, architecture, and engineering roadmap
- Build and launch the initial software product from 0 to 1
- Personally contribute to software development and technical architecture
- Select the technology stack and engineering infrastructure
- Build scalable, secure, cloud\-based systems
- Develop AI\-powered workflow automation and intelligent decisioning
- Build integrations with healthcare, billing, claims, payment, and financial systems
- Support healthcare data ingestion, normalization, and processing
- Design solutions for claims, denials, reimbursement, reconciliation, and payment workflows
- Recruit and lead the initial engineering team
- Work directly with the CEO, customers, investors, advisors, and commercial partners
- Take the product from MVP to a scalable commercial SaaS platform
Required Qualifications
- Proven experience building and launching a software product from concept or MVP to production
- Strong hands\-on software engineering and coding background
- Experience designing secure, scalable software architecture
- Experience building AI\-powered applications, automation platforms, intelligent workflows, or data\-intensive systems
- Experience owning technology strategy and product engineering roadmaps
- Experience hiring, building, or leading engineering teams
- Strong startup mindset with the ability to operate in a fast\-moving, ambiguous environment
- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- Strong communication skills with founders, customers, investors, and executive stakeholders
Preferred Experience
Candidates with experience in any of the following areas are strongly encouraged to apply:
- Healthcare technology
- Healthcare payments
- Revenue Cycle Management (RCM)
- Medical claims technology
- Provider\-payer workflows
- Payment reconciliation
- Denials management
- Healthcare billing
- FinTech or payment technology
- B2B SaaS
- AI workflow automation
- Enterprise software
- Data infrastructure
- Healthcare interoperability
- Secure cloud platforms
- Transaction\-heavy or regulated technology environments
Direct healthcare payments or revenue cycle experience is preferred, but candidates with strong experience in FinTech, payments, AI automation, enterprise SaaS, regulated systems, or transaction\-heavy platforms will also be considered.
Ideal Candidate
The ideal candidate may currently or previously have worked as a:
- CTO
- Technical Co\-Founder
- Founding Engineer
- Head of Engineering
- VP of Engineering
- Engineering Lead
- Principal Engineer
- Senior Software Architect
We are particularly interested in candidates who have worked in early\-stage startups or high\-growth technology companies and can demonstrate strong hands\-on ownership of product development.
A strong technical or academic background from a highly regarded computer science or engineering program is preferred.
What Success Looks Like
You will help transform a validated healthcare technology thesis into a commercial product by:
- Shipping the first production\-ready platform
- Building secure and scalable infrastructure
- Introducing AI and automation into healthcare payment workflows
- Establishing the early engineering organization
- Developing integrations across healthcare financial systems
- Creating the technical foundation for long\-term company growth
Work Location
This role is in\-person in New York City.
Candidates already located in the New York metropolitan area are strongly preferred. Candidates who can independently relocate may also be considered.
Visa sponsorship and relocation assistance are not available for this position.
Search Keywords
CTO, Chief Technology Officer, CTO Co\-Founder, Technical Co\-Founder, Founding Engineer, Head of Engineering, VP Engineering, Healthcare AI, HealthTech, Healthcare Payments, Revenue Cycle Management, RCM, Claims Technology, Payment Technology, FinTech, Artificial Intelligence, AI Automation, Workflow Automation, B2B SaaS, Healthcare SaaS, Engineering Leadership, Software Architecture, Healthcare Technology, New York CTO, NYC Startup Jobs
Pay: $141,000\.00 \- $151,000\.00 per year
Work Location: In person
Salary Context
This $141K-$151K range is below the median 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 LRx Healthcare, 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 ($146K) sits 32% below the category median. Disclosed range: $141K to $151K.
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.
LRx Healthcare AI Hiring
LRx Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $151K - $151K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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