Interested in this AI/ML Engineer role at HealthEdge Software, Inc.?
Apply Now →Skills & Technologies
About This Role
Overview:
HealthEdge® offers AI-powered operational infrastructure for health insurance companies, guaranteeing an enduring financial edge in an increasingly competitive market. We're experiencing strong market momentum, with a growing number of health plans choosing HealthEdge to modernize their operations and compete more effectively. As we expand, we're investing in the people who power that growth, making this a pivotal moment to join us and shape the future of healthcare technology. Learn more at HealthEdge.com.
As a Machine Learning Engineer, you will design, build, and ship AI agents and automation that solve real problems across HealthEdge's engineering, product, and delivery organizations, including customer-facing operations. You'll partner directly with stakeholders across Engineering, Product, and healthcare professionals to understand their workflows, identify high-leverage opportunities, and deliver working solutions end-to-end. Your growing expertise in machine learning and agentic AI will have a direct impact on how HealthEdge builds software, delivers for customers, and operates at scale. Key Responsibilities* AI Platform Development: Develop and implement AI Agents and automation that accelerates internal engineering workflows and customer facing delivery processes, owning the full lifecycle from problem discovery, through prototyping, evaluation, hardening, and production deployment. Contribute reusable libraries, prompt templates, tool-use patterns, and evaluation scaffolding back to the AI Platform.
- Integration: Partner with software engineers to integrate AI into the company's existing software infrastructure, supporting seamless functionality and performance.
- Collaboration: Work directly with product managers, implementation consultants, engineers, and business operations teams to identify pain points, scope solutions, and iterate toward measurable outcomes. You are the bridge between what AI can do and what the business needs done.
- Research and Learning: Stay current with advancements in LLMs, agentic frameworks, machine learning, and healthcare technology, and apply new knowledge to contribute ideas for innovation within the team.
- Performance and Reliability: Optimize AI systems for accuracy, latency, cost, and safety, with particular attention to human-in-the-loop design and guardrails appropriate for healthcare.
- Documentation: Maintain clear documentation of model development processes, methodologies, and results to ensure transparency and reproducibility.
Required Qualifications* Education: Master's degree in Computer Science, Machine Learning, Data Science, or a related field. A Bachelor's degree with relevant experience will also be considered.
- Experience: 2–4 years of experience building and deploying ML or AI systems in production. Experience working directly with non-technical stakeholders or in embedded/consulting-style engineering roles is a strong plus.
- Technical Skills: Strong proficiency in Python. Experience with LLM APIs, agentic frameworks (LangChain, Strands, etc.), and prompt engineering alongside traditional ML frameworks (PyTorch, scikit-learn, etc.). Solid software engineering fundamentals — version control, testing, CI/CD, and comfort operating across the full development lifecycle.
- Healthcare Knowledge: Interest in or familiarity with healthcare data, clinical workflows, and regulatory requirements. Experience working with electronic health records (EHR) or other healthcare datasets is a plus but not required.
- Analytical Skills: Strong problem-solving skills and the ability to work with complex datasets to derive actionable insights.
- Communication: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.
- Builder Mindset: Energized by turning ideas into working solutions. You balance speed with quality, thrive in ambiguous problem spaces, and pick up new domains quickly.
- Team Player: Ability to work collaboratively in a cross-functional team environment, accept feedback, and contribute to the success of the team.
Geographic Responsibility: Remote, USType of Employment: Full-time, permanentFLSA Classification (USA Only): ExemptWork Environment: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job:* The employee is occasionally required to move around the office. Specific vision abilities required by this job include close vision, color vision, peripheral vision, depth perception, and ability to adjust focus.
- Work across multiple time zones in a hybrid or remote work environment.
- Long periods of time sitting and/or standing in front of a computer using video technology.
- May require travel dependent on company needs.
The above statements are intended to describe the general nature and level of the job being performed by the individual(s) assigned to this position. They are not intended to be an exhaustive list of all duties, responsibilities, and skills required. HealthEdge reserves the right to modify, add, or remove duties and to assign other duties as necessary. In addition, reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position in compliance with the Americans with Disabilities Act of 1990. Candidates may be required to go through a pre-employment criminal background check. HealthEdge is an equal opportunity employer. We are committed to workforce diversity and actively encourage all qualified persons to seek employment with us, including, but not limited to, racial and ethnic minorities, women, veterans and persons with disabilities. #LI-Remote The annual US base salary range for this position is $X to $135,000. This salary range may cover multiple career levels at HealthEdge. Final compensation will be determined during the interview process and is based on a combination of factors including, but not limited to, your skills, experience, qualifications and education.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HealthEdge Software, Inc., 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
HealthEdge Software, Inc. AI Hiring
HealthEdge Software, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.