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About This Role
Start your next chapter at Revecore! For over 25 years, we've been at the forefront of specialized claims management, helping healthcare providers serve more patients by helping them recover more revenue. We're powered by people, driven by technology, and dedicated to our clients and employees. If you're looking for a collaborative and diverse culture with a great work/life balance, look no further.
Revecore Perks:
- We offer paid training and incentive plans
- Our medical, dental, vision, and life insurance benefits are available from the first day of employment
- We enjoy excellent work/life balance
- Our Employee Resource Groups build community and foster a culture of belonging and inclusion
- We match 401(k) contributions
- We offer career growth opportunities
- We celebrate 12 paid holidays and generous paid time off
Location: Remote – USA
As a Senior Machine Learning Engineer (individual contributor) at Revecore, you will:
Use your expertise in machine learning, exploratory data analysis, and software engineering to enhance the productivity and efficiency of our underpayment business. You will work on projects with purpose, such as prioritizing claims based on expected recovery dollars and improving our claim\-remit matching process.
This is a modeling team that owns the model deployment process. You won't be creating dashboards or pivot tables. You won't just build POCs. Our team increases revenue and decreases costs: you will deploy your work and see the results as we increase our client hospitals' revenue.
The Role:
Own end\-to\-end development, training, deployment, evaluation, and improvement of machine learning systems to rank claim opportunities.
Analyze and explore data to identify actionable opportunities from internal and 3rd party data.
Research, implement, and launch new model architectures that drive business impact.
Partner and collaborate with cross\-functional teams of software engineers, data engineers, subject matter experts, product managers, and analysts to design and build practical solutions.
Implement cloud MLOps and AIOps best practices to streamline the development, deployment, and maintenance of machine learning models.
Continuously measure the impact of the AI\-enabled workflows on key business metrics and use these measurements to improve the machine learning models and workflows.
Learn from and teach your teammates. You will be the team's expert in your specialization, and you will learn from experts in theirs.
Own a workstream. You'll be the technical lead for the workstream, partnering with others to deliver. You'll also work on other projects, but this workstream will be one of your key successes.
You'll be successful if you have:
An urge to question assumptions, and to get it right (or at least good enough) even if your first idea is wrong.
A commitment to collaborate, rather than go off in a corner only to appear when you need to submit a pull request.
A bachelor's degree in any data\-centric field. Scientific thinking is a must.
Experience working in a similar role, with a focus on machine learning or data science.
Experience developing and deploying machine learning models in a production environment.
Strong experience with Python, including scikit\-learn. TensorFlow or PyTorch is a plus.
Ability to wrangle data, perform exploratory data analysis, and draw insights from visualizations.
It would also be great if you have:
Intuition about data developed by doing statistics and/or research.
Applied experience with contemporary natural language processing (NLP) techniques and tools (e.g., entity extraction, transformers, Hugging Face).
Experience with Spark.
Experience with operating ML pipelines in a cloud platform (e.g., AWS, GCP, Azure).
A master's degree, Ph.D., or other experience demonstrating scientific thinking.
As part of our team, you'll be rewarded with:
Comprehensive medical, dental, vision, and life insurance benefits from the start of your employment.
12 paid holidays and flexible paid time off.401(k) contributions.
Employee Resource Groups that build community.
Career growth opportunities.
An excellent work/life balance.
Work at Home Requirements:
A quiet, distraction\-free environment to work from in your home.
A secure home internet connection with speeds \>20 Mbps for downloads and \>10 Mbps for uploads is required.
The workspace area accommodates all workstation equipment and related materials and provides adequate surface area to be productive.
Revecore is an equal opportunity employer that does not discriminate based on race, color, religion, sex or gender, gender identity or expression, sexual orientation, national origin, age, disability status, veteran status, genetic information, or any other legally protected status.
We believe that a diverse workforce fosters innovation and creativity, enriches our culture, and enables us to better serve the needs of our clients and communities. We welcome and encourage individuals of all backgrounds, perspectives, and abilities to apply.
Revecore is an equal opportunity employer that does not discriminate based on race, color, religion, sex or gender, gender identity or expression, sexual orientation, national origin, age, disability status, veteran status, genetic information, or any other legally protected status.
We believe that a diverse workforce fosters innovation and creativity, enriches our culture, and enables us to better serve the needs of our clients and communities. We welcome and encourage individuals of all backgrounds, perspectives, and abilities to apply.
Must reside in the United States within one of the following states:
Alabama, Arkansas, Delaware, Florida, Georgia, Iowa, Illinois, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan, Minnesota, Missouri, Mississippi, Montana, North Carolina, Nebraska, New Hampshire, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Wisconsin, or West Virginia.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Revecore, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Revecore AI Hiring
Revecore 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 $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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