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About This Role
### Description
At RXNT, we see ourselves as more than a healthtech company—we’re the digital backbone of the U.S. healthcare system. Every day, our platforms empower healthcare professionals to deliver better patient care, streamline medication and lab ordering, simplify billing and insurance processes, and strengthen connections across the entire healthcare ecosystem.
For over 25 years, we’ve been innovating at the intersection of healthcare and technology. That history has taught us a profound truth: transforming healthcare is both a privilege and a responsibility. It requires us to uphold the highest standards, think with rigor, and move with urgency when patients’ lives are on the line.
As we expand our AI\-driven product offerings, we’re tackling some of the most exciting and complex challenges in machine learning and healthcare. Saving the healthcare providers hours of after\-hours work and burnout, providing the means for a patient\-focused care, and improving the care accuracy and reliability are but a few of key values we are offering the health system today using AI.
We’re looking for curious, self\-motivated Senior Machine Learning Engineers who thrive in fast\-moving environments, embrace ownership, and never stop learning. You’ll join a team that combines the energy of a startup with the stability and trust of a company that has spent decades shaping healthcare technology. Led by seasoned entrepreneurs and technologists, you’ll have the opportunity to push boundaries, solve meaningful problems, and help define the future of AI in healthcare.
We're looking for an experienced MLE:* Has a proven track record of delivering mission\-critical models deployed at scale.
- Have strong foundational understanding of machine learning disciplines, advanced ML techniques, and advanced prompt engineering.
- Understands the full ML lifecycle—from data analysis to model fine\-tuning, evaluations and deployment, you will own the end\-to\-end development of advanced models.
- Productionize models—strong hands\-on production skills.
- Is excited about orchestrating (self\-improving) AI agents and fine\-tuning, evaluating and deploying models in production environments.
Location: Remote, USA ( this is a remote role; however, due to current employment and tax considerations, we are only able to hire candidates residing in the following U.S. states: Alabama, Arkansas, Colorado, District of Columbia, Florida, Georgia, Maryland, Minnesota, North Carolina, Ohio, Pennsylvania, Tennessee, Texas, Virginia, West Virginia, and Wisconsin).### Key Responsibilities
1\. Model Fine\-Tuning* Fine\-tune, and optimize various multi\-modal generative models, for various use cases.
- Experiment with techniques to enhance performance while keeping a production\-first mindset.
2\. Generative AI Orchestration* Build and manage ML graphs/workflows using orchestration frameworks like LangGraph or similar tools to enable AI\-driven agents.
3\. Building for Production* Write robust Python code that meets high standards and familiarity with software engineering principles.
- Collaborate with software engineers to integrate the ML models into larger, distributed systems.
- Adhere to best practices in code reviews, version control, CI/CD, and testing.
4\. ML Pipeline Management* While MLOps is not your primary role, you should have a strong foundation in full ML lifecycle and be familiar with cloud platforms (AWS, GCP, Azure), containerization, and monitoring to ensure models run reliably in production.
5\. Collaboration \& Leadership* Work closely with data teams, software engineers, and product teams to design and deliver impactful AI features.
- Champion best practices in machine learning.
- Contribute to the direction of the company’s AI strategy.
### Skills, Knowledge and Expertise
1\. Professional Background* 5\+ years of professional experience in machine learning engineering and/or data science.
2\. Education:* Preferably MS or PhD in computer science, math, artificial intelligence, or similar disciplines; or BSc with equivalent experience.
3\. Machine Learning Expertise* Strong knowledge of ML fundamentals: model architectures, data analysis, evaluation methods, and strong statistics and probabilities foundations.
- Proficient in one or more ML frameworks (e.g., PyTorch, TensorFlow, JAX) and advanced techniques for fine\-tuning.
- Expert at building accurate and high performing models, including evaluation, profiling, iterating, and monitoring.
4\. MLOps \& Cloud Know\-How* While not the primary focus, you’re comfortable with containerization, orchestration, and basic cloud services.
- Experience with monitoring, logging, and CI/CD pipelines to keep ML systems healthy and up\-to\-date.
5\. Mindset \& Culture* First\-principles problem solving: you can start from ambiguity and raw problems and work, with minimal supervision, with product and engineering partners to shape them into clear, impactful solutions.
- Bias to action: you excel at moving quickly and pragmatically to solve complex problems.
- Attention to detail: you value clean, maintainable solutions that can scale.
- Ownership: you take pride in your work, your team’s and your company’s problems are your problems, and you thrive in a collaborative environment.
- Adaptability: you enjoy learning and can pivot quickly as new challenges arise.
- Continuous learning: Stay up to date with the latest AI research, sharing expertise and fostering technical growth within the team.
Nice to have:* Hands\-on experience with LLMs, Generative AI, or similar advanced ML domains is a strong advantage.
- Experience with multimodal AI is a strong advantage.
- Experience leading or mentoring peers on technical projects.
### Benefits
RXNT offers employees access to medical insurance, 401K, short and long term disability, as well as the potential to earn quarterly incentives based on the company's performance.
Target Compensation: $150,000 \- $180,000 USD base salary \+ profit sharing bonus### About RXNT
Since 1999, RXNT has developed proven, certified healthcare technology for tens of thousands of physicians and medical billing professionals. We are an agile organization that prides itself on resolving customer challenges through collaboration and innovation, leading to better patient care. Each team member brings a unique set of skills, qualifications, and creativity to their role. But it’s the sum of our collective efforts that has propelled RXNT to leadership in the medical software industry.
Our leadership is being noticed; RXNT has been named to *Inc.* Magazine’s list of America’s 5000 Fastest\-Growing Private Companies multiple years in a row. We have also been recognized by organizations like Surescripts, Gartner Research, and Digital.com.
Reaching Common Goals Together
We develop technology that helps create a healthier world for people of all ages, gender identities, races, ethnicities, cultures, religions, sexual orientations, and perspectives. To be successful, we need a diverse workforce that represents the practices and patients who benefit from our software. When you become an RXNT team member, we’ll empower you to lean on your experience, skills, diversity, and expertise to drive excellence and creativity in every aspect of your work.
We strive to offer a workplace that fosters personal and professional growth. RXNT welcomes your uniqueness because we know that’s what stimulates the innovation and imaginative problem\-solving that helps us achieve our goals, and create a better future.
Salary Context
This $150K-$180K 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 RXNT, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 9% below the category median. Disclosed range: $150K to $180K.
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.
RXNT AI Hiring
RXNT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Annapolis, MD, US. Compensation range: $180K - $180K.
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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