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About This Role
We anticipate the application window for this opening will close on - 16 Oct 2026 At MiniMed, you can begin a lifelong career of exploration and innovation, while helping make a difference in the lives of people living with diabetes around the globe. You'll lead with purpose, breaking down barriers to innovation for a more connected, compassionate world.
About the Role
Join MiniMed for a summer internship where you’ll do meaningful work, gain hands-on experience, and make an impact on our mission to make every day a better day for people with diabetes. You’ll be part of a collaborative intern community, build your professional network, and gain exposure to opportunities that help you grow both personally and professionally.
Our summer internships are full-time (40 hours/week), paid positions lasting 10-11 weeks. We offer competitive compensation, educational and social programming throughout the summer, as well as housing assistance and relocation support for eligible interns. At MiniMed , we're transforming how a medical device company operates through artificial intelligence, and we want you to help drive that transformation.
As an AI Engineer Intern , you'll be embedded within MiniMed's AI & Data Science team , working at the intersection of cutting-edge AI technology and real-world business impact. This is a hands-on, build-and-ship role, not a research or observation role. You'll own a scoped project from problem definition to prototype development and evaluation, partnering with technical teams and business stakeholders to deliver measurable business value.
Your work will span tw o of our core AI focus areas:
- AI Agents and Tools: Design, build, and evaluate LLM-powered agents on our MIA stack. You’ll work with industry-standard technologies, including AWS Services, Databricks and LangGraph , to develop reliable solutions for real business needs.
- Enterprise AI Enablement: Help expand self-service AI adoption across the enterprise by building and deploying reusable agents and function-specific skills. You’ll empower teams to apply AI in their day-to-day workflows and improve how they work.
Project examples may include:
- Prototyping a domain-specific AI agent for a business function
- Developing and running evaluation test sets to measure agent quality, detect hallucinations, and identify regressions
- Building reusable agent skills and applying guardrails to ensure responsible, auditable outputs
Along the way, you'll gain practical grounding in evaluation-driven development, exposure to responsible AI delivery in a regulated medical-device environment (privacy, auditability, human-in-the-loop design), and real-world experience translating a business need into a shipped, measurable solution. You'll leave with a strong portfolio of applied AI work and a deeper understanding of how enterprise AI strategy gets executed in a complex, compliance-driven industry.
Location and Work Model
This internship is based in John's Creek , GA . All interns must reside in the U.S. for the duration of the internship. Interns are expected to work onsite a minimum of 3 days per week and participate full time, 40 hours per week, during the summer of 2027.
Must Have: Minimum Requirements
To be considered for a Summer 2027 AI Engineer internship, you must meet the following requirements:
- Working toward a Master’s degree in Computer Science, Data Science, Machine Learning, AI, or a related discipline with an anticipated graduation date of Winter 2027 or Spring 2028.
- Applicants for MiniMed’s Summer Internship Program must be currently authorized to work in the United States. MiniMed does not sponsor work authorization for internship positions. Individuals on temporary visa classifications, including E, F‑1 (CPT or OPT), H‑1, H‑2, L, B, J, or TN, as well as anyone who requires employment sponsorship now or in the future, are not eligible for MiniMed’s Summer Internship Program.
Nice to Have
- Strong Python programming skills
- Familiarity with AI/ML concepts including; LLMs, prompt engineering, RAG, deep agents and model evaluation
- Exposure to tools such as Databricks, Spark, LangGraph, LangSmith, vector databases, or SQL
- Coursework or project experience in machine learning, NLP, deep learning, statistics, and algorithms
- Strong problem-solving skills, curiosity, and the ability to navigate ambiguous, real-world challenges
- Effective communication and collaboration skills
#MiniMed #Intern #Internship
Physical Job Requirements
The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position.
The physical demands described within the Responsibilities section of this job description are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. For Office Roles: While performing the duties of this job, the employee is regularly required to be independently mobile. The employee is also required to interact with a computer and communicate with peers and co-workers. Contact your manager or local HR to understand the Work Conditions and Physical requirements that may be specific to each role.
Benefits & Compensation
MiniMed offers a competitive salary and flexible benefits package
At MiniMed, we put people first. A commitment to our employees lives at the core of our values: We recognize their contributions. They share in the success they help create. We offer a wide range of benefits, resources, and competitive compensation plans designed to support you at every stage of your career and life.
Salary ranges for U.S (excl. PR) locations (USD): The MiniMed Internship Program offers an hourly rate of pay that falls within the range of $26.50 to $45.25 per hour. The hourly rate offered will be determined by the position and is subject to variation based on the number of years of school completed. Compensation and benefits information pertains solely to candidates hired within the United States (local market compensation and benefits will apply for others).
At MiniMed, we are committed to supporting the well-being and financial security of our employees. Regular employees working 20 or more hours per week are eligible for a robust benefits package, including health, dental, and vision insurance, as well as access to a Health Savings Account, Healthcare Flexible Spending Account, life insurance, long-term disability leave, and a dependent daycare spending account. In addition, all regular employees enjoy incentive plans, a 401(k) plan with company match, short-term disability coverage, paid time off and holidays, participation in our Employee Stock Purchase Plan, and access to our Employee Assistance Program. Eligible employees may also benefit from our Non-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements. Please note that “regular employees” refers to those who are not temporary staff, such as interns, and some benefits may not apply to employees in Puerto Rico.
For further details about our comprehensive benefits, we encourage you to visit the link below.
MiniMed Benefits Overview
About MiniMed
MiniMed is a full-stack insulin delivery company dedicated to supporting people living with diabetes through every step of their journey — when and how they need it. For more than 40 years, we’ve been committed to redefining what’s possible: intelligent dosing systems designed for real life, predictive insights that stay a step ahead, and always on support when it’s needed most. At the heart of everything we do is a simple Mission: to make every day a better day for people with diabetes.
Learn more about our business, and our mission here .
It is the policy of MiniMed to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, familial status, membership or activity in a local human rights commission, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, MiniMed will provide reasonable accommodations for qualified individuals with disabilities.
If you are applying to perform work for MiniMed in any position which will involve performing at least two (2) hours of work on average each week within the unincorporated areas of Los Angeles County, you can find here a list of all material job duties of the specific job position which MiniMed reasonably believes that criminal history may have a direct, adverse and negative relationship potentially resulting in the withdrawal of a conditional offer of employment. MiniMed will consider for employment qualified job applicants with arrest or conviction records in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Salary Context
This $54K-$93K 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 MiniMed, 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 ($73K) sits 65% below the category median. Disclosed range: $54K to $93K.
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.
MiniMed AI Hiring
MiniMed has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Northridge, CA, US, Johns Creek, GA, US. Compensation range: $93K - $190K.
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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