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About This Role
Job Summary:
The Applied AI Scientist IV is responsible for establishing technical direction and leading the design, development, and operationalization of advanced artificial intelligence solutions that improve healthcare operations, patient outcomes, and organizational performance. This role provides enterprise-wide technical leadership across machine learning, Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP) initiatives. The Applied AI Scientist IV establishes technical standards, mentors scientific teams, drives innovation, and serves as a strategic advisor for AI adoption and transformation efforts across the organization.
Essential Functions:* Lead evaluation of emerging AI technologies and define adoption strategies that drive innovation and business value across the enterprise.
- Utilize NLP algorithms and other deep learning techniques to process and analyze unstructured healthcare data, such as clinical notes, patient feedback, and medical literature, to extract meaningful insights.
- Provide technical leadership for AI solutions from ideation through enterprise-scale production deployment in partnership with engineering and architecture teams.
- Influence enterprise AI roadmaps through thought leadership, technical expertise, and strategic partnership with business and technology leaders.
- Establish technical standards, reusable frameworks, and best practices for development, deployment, monitoring, and governance of AI solutions.
- Define standards and best practices for operational readiness, evaluation, monitoring, governance, documentation, and post-deployment optimization of AI solutions.
- Direct highly complex NLP, predictive analytics, and applied AI initiatives impacting multiple business domains and strategic priorities.
- Work closely with interdisciplinary teams across IT, risk adjustment, program integrity, HEDIS, healthcare operations, finance, and clinical departments to identify and capitalize on opportunities for data-driven innovative solutions using cutting-edge emerging technologies.
- Develop and implement predictive models, algorithms, and statistical techniques to extract insights from large and complex healthcare datasets.
- Define and execute evaluation strategies for ML and LLM solutions, including quality metrics, bias and safety checks, and performance monitoring after deployment.
- Utilize machine learning algorithms to identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care.
- Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations.
- Collaborate with stakeholders to define key performance indicators (KPIs), develop metrics, and create dashboards and reports that effectively communicate insights and support decision-making.
- Provide strategic guidance and recommendations to senior leadership based on data analysis and predictive modeling results.
- Ensure compliance with data privacy and security regulations, including HIPAA and PHI handling as applicable and maintain the highest standards of data integrity, model governance, and documentation.
- Influence enterprise AI strategy through technical leadership across multiple programs, initiatives, and organizational priorities.
- Serve as a recognized subject matter expert for AI, machine learning, NLP, Generative AI, and emerging technologies across the organization.
- Mentor Applied AI Scientists, Data Scientists, and technical teams through code reviews, technical guidance, and architecture recommendations.
- Evaluate Responsible AI practices, emerging healthcare AI regulations, and governance requirements to ensure compliant and trustworthy AI solutions.
- Perform any other job related duties as requested.
Education and Experience:* Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
- Master's degree preferred
- Equivalent years of relevant work experience may be accepted in lieu of required education
- Eight (8) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations, required
- Three (3) years of experience in the healthcare industry required
- Three (3) years of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflake) required
- Three (3) years of experience in development and deployment of an NLP and Generative AI solution in the healthcare industry required
- Experience providing technical leadership, architecture guidance, and mentoring scientific teams required
Competencies, Knowledge and Skills:* Expert knowledge of Agentic AI, LLM architectures, prompt engineering, Retrieval-Augmented Generation (RAG), evaluation methodologies, and AI solution design
- Advanced expertise in MLOps and LLMOps practices including model deployment, monitoring, experiment tracking, reproducibility, CI/CD, and governance
- Advanced knowledge of model risk management, AI governance, Responsible AI, and healthcare AI regulatory considerations
- Expert knowledge of statistical learning, machine learning, predictive analytics, and scientific computing using Python and/or R
- Expert in data manipulation, data visualization, and SQL for data extraction and analysis
- Ability to perform advanced statistical analysis and modeling such as linear and non-linear regression, sampling, and Markov chains
- Expertise designing document intelligence, OCR, and information extraction solutions utilizing modern AI technologies.
- Expertise in AI solution architecture, API integration, and deployment of production-grade AI applications.
- Detailed knowledge of healthcare data, including medical and pharmacy claims, EMR data, HIE data, UM data, and demographic and population data
- Knowledge of healthcare operations, payer and provider models, and industry trends
- Proficient in feature engineering techniques and exploratory data analysis
- Excellent analytical, problem-solving, and critical-thinking skills
- Strong technical leadership, influence, and program coordination skills
- Excellent written and verbal communication and presentation skills
Licensure and Certification:* None
Working Conditions:* General office environment; may be required to sit or stand for extended periods of time
- Up to 15% (occasional) travel to attend meetings, trainings, and conferences may be required
Compensation Range:
$113,000.00 - $197,700.00 CareSource takes into consideration a combination of a candidate’s education, training, and experience as well as the position’s scope and complexity, the discretion and latitude required for the role, and other external and internal data when establishing a salary level. In addition to base compensation, you may qualify for a bonus tied to company and individual performance. We are highly invested in every employee’s total well-being and offer a substantial and comprehensive total rewards package.
Compensation Type (hourly/salary):
Salary Organization Level Competencies
- Fostering a Collaborative Workplace Culture
- Cultivate Partnerships
- Develop Self and Others
- Drive Execution
- Influence Others
- Pursue Personal Excellence
- Understand the Business
This job description is not all inclusive. CareSource reserves the right to amend this job description at any time. CareSource is an Equal Opportunity Employer. We are dedicated to fostering an environment of belonging that welcomes and supports individuals of all backgrounds.
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Brand=CareSource
Salary Context
This $113K-$197K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At CareSource, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $113K to $197K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
CareSource AI Hiring
CareSource has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $197K - $197K.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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