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About This Role
Company Overview:
Verathon is a global medical device company focused on supporting customers by being their trusted partner, delivering high\-quality products that endure over time and ensure clinical and economic utility. Two areas where Verathon has significantly impacted patient care, and become the market leader in each, are bladder volume measurement and airway management. The company’s BladderScan portable ultrasound and GlideScope video laryngoscopy \& bronchoscopy systems effectively address unmet needs for healthcare providers and meaningfully raise the standard of care for patients. Verathon, a subsidiary of Roper Technologies, is headquartered in Bothell, Washington, USA and has international subsidiaries in Canada, Europe and Asia Pacific. For more information, please visit www.verathon.com.
Overview:
The AI Engineer plays a critical hands\-on role in bringing Verathon’s AI strategy to life. This role is responsible for building, configuring, and deploying AI\-powered solutions, including custom GPTs, copilots, automations, applications, and agentic workflows, that solve high\-impact problems across the company. The AI Engineer partners closely with AI Business Partners and the AI Solutions Architect to translate business needs into scalable, secure, and compliant technical solutions.
This position is ideal for a practical builder who thrives in fast experimentation, iterative development, and enabling real\-world value through AI. It includes technical context engineering, assembling prompts, instructions, retrieval logic, and agent workflows that reflect Verathon’s processes and domain knowledge.
Responsibilities:
- Design, develop, and deploy AI\-powered solutions including custom GPTs, copilots, and agentic workflows using enterprise LLM platforms and APIs.
- Build Retrieval\-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon\-owned data usage.
- Collaborate with AI Business Partners and other departmental representatives to translate functional requirements and use\-case concepts into technical implementations.
- Translate business\-defined context into structured prompts, system instructions, agent behaviors, and RAG retrieval logic (“technical context engineering”).
- Refine and optimize context injection strategies (prompt templates, chains, tools, memory, retrieval parameters) to improve solution reliability and accuracy.
- Prototype rapidly, iterate based on user feedback, and deliver production\-ready AI solutions with appropriate guardrails.
- Develop reusable prompts, templates, automations, and components to accelerate future AI solution development.
- Integrate AI tools with Verathon systems (e.g., Salesforce, Epicor, MasterControl, HRIS) following architecture, security, and compliance standards.
- Partner with the AI Solutions Architect to evaluate and implement new AI tools and ensure alignment with Verathon’s AI Policy and Roper guidelines.
- Document solutions clearly and support functional teams through adoption, training, and scaling of deployed AI tools.
Qualifications:
- Bachelor’s degree in computer science, engineering, information systems, data science, or related field.
- 3–7 years of experience in software development, automation engineering, data engineering, or applied AI development.
- Hands\-on experience with Python, REST APIs, and modern AI/LLM frameworks (e.g., LangChain, Semantic Kernel, LlamaIndex).
- Experience configuring and deploying AI tools such as ChatGPT Enterprise, Copilot, Claude, or similar enterprise AI platforms.
- Understanding of Retrieval\-Augmented Generation (RAG), vector databases, and prompt design best practices.
- Familiarity with integration patterns for enterprise systems and cloud environments (Azure/AWS).
- Demonstrated ability to rapidly experiment, prototype, and iterate AI solutions based on business feedback.
- Strong collaboration skills and comfort working closely with business and technical stakeholders.
Who You Are
- A practical builder who enjoys turning ideas into working solutions quickly.
- Comfortable navigating ambiguity and translating loosely defined requirements into prototypes and production\-ready tools.
- Hands\-on with modern AI tools and excited about learning new technologies.
- Driven by impact; motivated to deploy solutions that make work faster, easier, and smarter across the organization.
- A collaborative partner who communicates clearly and earns trust through delivery.
*Salary range \- $115,000\.00 \- $231,000\.00 (Compensation will vary based on skills, experience and location; it is not typical to be hired at or above the top of the salary range).*
Full\-time employees who are not on a commission plan are eligible for Verathon’s annual bonus plan based on company and individual performance.
Verathon provides a competitive benefits package including medical, dental, vision, basic life insurance, paid holidays, paid time off and a 401(k) matching plan. For more information, please visit our complete Benefits Summary at https://www.verathon.com/sites/default/files/2026\-02/US\_HQ\_Employee\_Benefits\_Summary.pdf.
EEO: Research shows that women and underrepresented groups tend to apply to jobs only when they check every box on a job posting. If you’re currently reading this and hesitating to click “Apply” for that reason, we encourage you to go for it! Even if you are not a match for this role, we may have another opportunity that may be a great fit. Why Verathon Verathon is committed to becoming an AI\-native organization where AI is how we work, grow, and win. Join us and help accelerate our transformation by building real AI solutions that unlock innovation, productivity, and operational excellence across healthcare technology. Verathon is an equal opportunity employer and strongly supports diversity in the workplace. We believe that diverse ideas, opinions and perspectives will build a strong foundation for success. In order to provide equal employment and advancement opportunities to all individuals, employment decisions at Verathon will be based on merit, qualifications, and abilities. Verathon does not discriminate in employment opportunities or practices on the basis of race, color, religion, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by law.
Salary Context
This $115K-$231K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Verathon, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($173K) sits 5% below the category median. Disclosed range: $115K to $231K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Verathon AI Hiring
Verathon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $231K - $231K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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