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About This Role
EZLabs, the AI innovation division of VetsEZ, is seeking multiple Applied AI Product Engineers to join our growing team in a 100% remote role focused on building intelligent healthcare technology platforms and next\-generation AI\-driven solutions. At EZ Labs, we are transforming healthcare delivery through technology, innovation, and compassionate design. We partner with care teams, providers, and payers to improve patient engagement, care coordination, and claims management through advanced analytics, intelligent automation, and secure digital platforms.
This role is ideal for a hands\-on engineer who thrives at the intersection of AI, product development, and full\-stack software engineering. The ideal candidate has practical experience building and deploying real\-world AI\-powered applications using modern AI development tools such as Cursor, Claude, Codex, OpenAI, and related LLM technologies.
The candidate must reside within the continental US.
Responsibilities:
- Design, develop, and implement AI\-powered features across healthcare product platforms and operational systems.
- Build full\-stack applications using modern AI engineering workflows and tools such as Cursor, Claude, Codex, OpenAI APIs, and related LLM technologies.
- Collaborate with product managers, designers, engineers, and clinical stakeholders to deliver scalable, user\-focused solutions that improve patient engagement, care coordination, and operational efficiency.
- Develop APIs, backend services, automation pipelines, and AI workflows that support intelligent product capabilities and production\-ready deployments.
- Apply software engineering, DevOps, and MLOps best practices to testing, deployment, monitoring, and optimization of AI solutions.
- Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks to continuously improve platform capabilities.
- Navigate ambiguity, solve complex technical challenges, and collaborate effectively across technical and non\-technical teams in a fast\-paced Agile environment.
- Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.
Requirements:
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
- 2\+ years of professional experience in software engineering, product engineering, or full\-stack application development.
- Hands\-on experience delivering at least 2–3 production\-level AI projects using tools such as Cursor, Claude, Codex, OpenAI, Anthropic, or similar AI development platforms.
- Strong programming skills with proficiency in Python, TypeScript, JavaScript, or related modern development languages, along with experience building and deploying production\-grade full\-stack applications.
- Experience integrating large language models (LLMs), prompt engineering, AI agents, generative AI workflows, or retrieval\-augmented generation (RAG) architectures.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, along with containerization and deployment technologies such as Docker and Kubernetes.
- Strong communication, collaboration, analytical, and problem\-solving skills with the ability to work independently in a remote Agile environment.
Additional Qualifications:
- Experience with AI\-assisted software development workflows, vector databases, embeddings, semantic search, orchestration frameworks, or AI workflow automation.
- Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Hugging Face, or scikit\-learn.
- Exposure to CI/CD pipelines, DevOps practices, infrastructure automation, or secure healthcare technology environments.
- Experience supporting healthcare, payer/provider, federal government, or Department of Veterans Affairs (VA) technology initiatives is preferred.
- Ability to obtain a government clearance is a plus.
Benefits:
- Medical/Dental/Vision.
- 401k with Employer Match.
- PTO \+ Federal Holidays.
- Corporate Laptop.
- Training opportunities.
- Remote Opportunity.
Note: Selected candidates will be required to complete fingerprinting at a government facility and undergo a background check as part of the hiring process.
VetsEZ is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status.
Sorry, we are unable to offer sponsorship at this time
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At VetsEZ, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
VetsEZ AI Hiring
VetsEZ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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