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About This Role
Make an Impact with AI/ML Engineering
Bring your curiosity, engineering skills, and bold ideas to Evernorth Health Services, a division of The Cigna Group. This internship is a foundational development opportunity for emerging AI/ML engineers ready to build scalable solutions for meaningful health care challenges. You will contribute to strategically important work, learn alongside ambitious and compassionate experts, and explore how modern AI, data, cloud, and software engineering can improve health outcomes and business decisions.
This is more than a summer project. Solutions developed by our teams have evolved into enterprise capabilities supporting critical business functions, and several projects have contributed to innovative intellectual property and patent filings. You will gain exposure to the full AI lifecycle while building relationships through professional development, networking, and collaboration with experienced engineers, data scientists, architects, and business stakeholders.
Responsibilities
- Build and improve reusable machine learning frameworks that accelerate model development and production deployment.
- Design scalable data pipelines and feature-engineering solutions using Python, SQL, Spark, Databricks, or related technologies.
- Develop production-ready APIs, cloud-native services, and intelligent applications that make AI capabilities easier to use across the enterprise.
- Contribute to MLOps automation, containerized deployment, monitoring, and reliable engineering practices that improve speed, quality, and repeatability.
- Work with large health care datasets, including pharmacy and medical claims, member information, call transcripts, surveys, and web logs, to identify actionable opportunities.
- Partner with technical and business stakeholders to translate complex needs into scalable, responsible solutions and communicate outcomes clearly.
- Explore generative AI, agentic AI, retrieval-augmented generation, document intelligence, or real-time prediction while seeking feedback and documenting what you learn.
Past projects have included Spark and Databricks feature-engineering platforms; AI/ML experimentation frameworks; MLOps and deployment automation; agentic and generative AI applications; enterprise chatbots; retrieval-augmented generation and document intelligence platforms; and real-time prediction services.
Required Qualifications
- Pursuing a master's degree or PhD in computer science, statistics, applied mathematics, engineering, operations research, bioinformatics, information systems, computational linguistics, or another quantitative field.
- At least 1 year of hands-on machine learning, data engineering, analytics, or software engineering experience gained through coursework, research, internships, or professional projects.
- Working knowledge of Python and SQL, with experience preparing data or building technical solutions.
- Experience developing or supporting machine learning, data engineering, or software engineering solutions through academic or applied projects.
- Ability to explain technical work clearly, collaborate across disciplines, and adapt based on feedback and new information.
Preferred Qualifications
- Experience with scikit-learn, MLlib, TensorFlow, PyTorch, AWS, Apache Spark, Databricks, PySpark, or Spark SQL.
- Experience developing APIs with FastAPI or Flask and using CI/CD tools such as Jenkins or GitHub Actions.
- Exposure to Docker, Kubernetes, Hive, Scala, HDFS, or other distributed computing and containerization technologies.
- Previous software engineering experience and a 3.0 GPA or higher with strength in quantitative coursework.
Additional Information
Location: This internship follows a hybrid schedule with three days per week in the Austin, TX; Morris Plains, NJ; or St. Louis, MO office.
Compensation: Hourly pay ranges from $35.00-$43.00, based on degree program and year of study.
Schedule: This is a full-time, 12-week summer internship working 40 hours per week beginning in May 2027.
Work Authorization: Candidates must be authorized to work in the United States and not require current or future employment sponsorship.
If you will be working at home occasionally or permanently, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload.About The Cigna Group
Doing something meaningful starts with a simple decision, a commitment to changing lives. At The Cigna Group, we’re dedicated to improving the health and vitality of those we serve. Through our divisions Cigna Healthcare and Evernorth Health Services, we are committed to enhancing the lives of our clients, customers and patients. Join us in driving growth and improving lives.Qualified applicants will be considered without regard to race, color, age, disability, sex, childbirth (including pregnancy) or related medical conditions including but not limited to lactation, sexual orientation, gender identity or expression, veteran or military status, religion, national origin, ancestry, marital or familial status, genetic information, status with regard to public assistance, citizenship status or any other characteristic protected by applicable equal employment opportunity laws.
If you need a reasonable accommodation to complete the online application process, please email [email protected] for assistance. Please note that this email inbox is dedicated to accommodation requests only and cannot provide application updates or accept resumes.
The Cigna Group has a tobacco-free policy and reserves the right not to hire tobacco/nicotine users in states where that is legally permissible. Candidates in such states who use tobacco/nicotine will not be considered for employment unless they enter a qualifying smoking cessation program prior to the start of their employment. These states include: Alabama, Alaska, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Iowa, Kansas, Maryland, Massachusetts, Michigan, Nebraska, Ohio, Pennsylvania, Texas, Utah, Vermont, and Washington State.
Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances.
Salary Context
This $72K-$89K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At The Cigna Group, 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. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($81K) sits 62% below the category median. Disclosed range: $72K to $89K.
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.
The Cigna Group AI Hiring
The Cigna Group has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Bloomfield, CT, US, Orlando, FL, US, Morris Plains, NJ, US. Compensation range: $89K - $219K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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
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