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About This Role
Overview:
The AI Engineer Intern focuses on researching and developing advanced healthcare informatics, particularly in the realm of emerging Artificial General Intelligence. It is designed to support improvements in healthcare treatment, payment, and operations. The intern will support multiple projects to set up the underlying architecture, such as context layer for data agents, setting up guardrails and governance. *We are currently looking for interns who can start immediately, 12 week internship, and work 40 hours per week.*
Responsibilities:
Technical Expertise
- SQL fundamentals — comfortable writing queries against structured data, not just describing them
- Exposure to LLM APIs (e.g., OpenAI, Azure OpenAI, Anthropic) and at least tutorial\-level familiarity with one orchestration framework (LangChain, LlamaIndex, or similar)
- Git basics — comfortable with branching and reviewing code as part of a team workflow
- Ability to reason about data quality, duplication, and freshness
- Fetch and ingest data from live external sources (APIs and web sources) into a structured pipeline — handling formats, rate limits, and failures, not just calling a pre\-built connector
- Design and build the schema and storage layer your pipeline writes to, in collaboration with data engineering
- Build and test AI components — embeddings, retrieval logic, prompts — using established patterns and tools
Communication \& Collaboration
- Communicates technology\-related updates and requirements to other departments and contributes to presentations for senior management.
- Collaborates with research and development teams, product management, and strategic analysts to support ongoing projects.
Leadership \& Management
- Partners with Business Units to provide reliable intelligence, validated technology options, and insights on enterprise and industry trends.
- Supports technology project teams by coordinating specific tasks, assisting with day\-to\-day operations, and contributing to the successful delivery of solutions.
- Assists in collaborative efforts with academic research teams, practicums, internships, and vendor POCs.
External Relationships \& Partnerships
- Supports vendor evaluations and contributes to collaborations with vendors.
- Assists as a liaison between academic institutions, professional organizations, and research groups as needed.
Other
- Support the Academic Corporate Engagement efforts to develop research and educational talent, with a focus on enhancing health tech knowledge and skills.
- Complete all responsibilities and goals outlined in the internship program.
- Complete all special projects and other duties as assigned.
- Must be able to perform duties with or without reasonable accommodation.
*This job description is intended to describe the general nature and level of work being performed in this internship. It is not an exhaustive list of responsibilities, duties, and skills required and does not constitute an employment agreement. This job description is subject to change as Cotiviti’s needs and requirements of the internship evolve.*
Qualifications:
- Currently pursuing or recently completed an advanced degree in healthcare, technology, or a related field (e.g., Biomedical Informatics, Computer Science) with preference of a PhD.
- Demonstrated interest or experience in AI, healthcare technology, or informatics research.
- Strong foundational knowledge in generative AI model development, architectures, and vector databases.
- Hands\-on experience with working with Machine Learning and Deep Learning models. Experience with LLM/RAG models and LLM fine\-tuning is a plus.
- Hands\-on experience working with cloud services (AWS/Azure), large data sets, and building data pipelines for ML solutions. Experience with vector embeddings and databases is a plus.
- Ability to work collaboratively and communicate effectively with cross\-functional teams.
Mental Requirements:* Communicating with others to exchange information.
- Assessing the accuracy, neatness, and thoroughness of the work assigned.
Physical Requirements and Working Conditions:* Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide a dedicated, secure work area.
- Must be able to provide high\-speed internet access/connectivity and office setup and maintenance.
- No adverse environmental conditions are expected.
Base compensation ranges from $32\.00 to $40\.00 per hour. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs.
Nonexempt employees are eligible to receive overtime pay for hours worked in excess of 40 hours in a given week, or as otherwise required by applicable state law.
Date of posting: 6/18/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 7/18/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.
\#LI\-MD1
\#LI\-remote
\#intern
Salary Context
This $66K-$83K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cotiviti, 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. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($74K) sits 66% below the category median. Disclosed range: $66K to $83K.
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.
Cotiviti AI Hiring
Cotiviti has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $83K - $280K.
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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