Interested in this AI/ML Engineer role at National Public Radio?
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OVERVIEW
*A thriving, mission\-driven multimedia organization, NPR produces award\-winning news, information, and music programming in partnership with hundreds of independent public radio stations across the nation. The NPR audience values information, creativity, curiosity, and social responsibility – and our employees do too. We are innovators and leaders in diverse fields, from journalism and digital media to IT and development. Every day, our employees and member stations touch the lives of millions worldwide.*
*Across our organization, we’re building a workplace where collaboration is essential, diverse voices are heard, and inclusion is the key to our success. We are committed to doing the right thing in our journalism and in every role at NPR**.* *This means that integrity, adherence to our ethical standards, and compliance with legal obligations are fundamental responsibilities for every employee at NPR.*
Internship Program Duration: October 5th, 2026 \- April 16th, 2027
Application Instructions: Applications close on Sunday, June 14th at 11:59 pm ET.
### Intro to Position
As an intern co\-hosted by the AI Labs and Research, Archives \& Data Strategy (RAD) teams, you will collaborate with a cross\-functional group of technologists, product managers and archivists to shape the future of ethical AI in journalism.
This internship consists of two parts. Each part will be hosted by a different team.
AI Labs, October\-January
You will use generative AI to surface archival content for contemporary use. You will partner with RAD to design and prototype workflows that transform unstructured archival data into structured, discoverable assets. Your work will empower newsroom staff to query historical content for current reporting while ensuring all technical prototypes uphold NPR’s journalistic standards.
RAD, January\-April
You will help us establish standards for the ethical use of artificial intelligence with NPR's archival content as we build a data infrastructure that remains grounded in journalistic ethics. You will prepare digital collections for machine learning and research approaches that pair AI with human editorial judgment and metadata to find new insights from the archive.
### Responsibilities
AI Prototyping \& Workflow Development
- Identify opportunities and build functional prototypes that enable reporters and producers to query historical audio for contemporary reporting
- Assist in the development of automated workflows to transform unstructured legacy media into a searchable, AI\-indexed library
- Contribute to the delivery of a technical blueprint demonstrating how NPR can effectively revitalize dormant audio assets for new podcasts or digital series
Data Analysis \& Integrity
- Analyze historical data troves to surface evergreen content opportunities and extract narrative leads that support contemporary reporting
- Evaluate AI outputs and discovery tools for accuracy and bias to ensure technical prototypes maintain archival integrity
- Act as a cross\-functional partner between AI Labs and the RAD team to ensure editorial utility in all AI\-driven prototypes
Archival Data Management \& Preparation
- Identify and prepare strategic components of our digital newsmagazine and podcast collections for experimentation with machine learning and generative AI applications
- Assist in the development of effective data structures and preparation methods, documentation, and standards for ethical AI use with archival news content
- Help determine optimal uses of controlled vocabularies in AI workflows with NPR content
This is an NPR editorial role covered under the terms of the NPR Ethics Handbook. All editorial staff are bound by this guidance. Editorial staff are defined as staff members who play a role in shaping the journalistic or creative direction of NPR's content, including events.
The above duties and responsibilities are not an exhaustive list of required responsibilities, duties and skills. Other duties may be assigned, and this job description can be modified at any time.
### Qualifications
- Demonstrated ability to experiment, evaluate and work with generative AI tools and technologies
- Familiarity with Natural Language Processing and Machine Learning techniques
- Knowledge of archiving and metadata management best practices
- Analytical skills to extract narrative leads and opportunities from historical data
- Familiarity with code repositories and version control systems, such as GitHub
- Demonstrated ability to work independently, manage projects with attention to detail, and meet deadlines
- Excellent written and verbal communication skills for drafting technical documentation and presenting research findings
- Collaborative mindset for working effectively in a remote, cross\-functional team and responding constructively to professional feedback
### Application Requirements
- Resume
- Cover Letter
- Portfolio Link (Preferred)
### Education Requirements
- Must be a current student in an accredited degree program or a recent graduate of no more than 12 months from the month of the start of the internship.
### Work Location \& Requirements
- NPR Remote\-Permitted: This is a remote\-permitted role. This role is based out of our Washington, D.C. office, but the employee may choose to work on a remote basis from a location that NPR approves. You will have the option of working (a) remotely from a location of your choosing within the United States that is supported by NPR; (b) on\-site at an NPR facility, based on the availability of desks and approval from NPR; or (c) a combination of both. Regardless of where you choose to work from, you may be expected to travel to other locations from time to time to perform the duties of your position.
### Schedule
- 30\-40 hours per week Monday\-Friday, during daytime working hours.
### Job Type
- This is a full\-time, non\-exempt internship.
### Compensation
Hourly Rate The U.S.\-based anticipated hourly rate for this opportunity is $20\.00 per hour. The range displayed reflects the minimum and maximum hourly rate NPR expects to provide for new hires for the position across all US locations.
NPR Benefits: NPR offers interns a robust package of benefits and resources designed to foster their professional development and support their overall well\-being. Interns are eligible to make elective contributions to the NPR 403(b) retirement plan. As an intern, you have access to Transit Commuter benefits through HealthEquity, which allow you to set aside pre tax funds for eligible commuting expenses. You are also eligible for membership with the Signal Financial Federal Credit Union, giving you access to a range of financial services and resources. At our DC Headquarters, interns enjoy free access to on\-site Wellness Center, free fitness center membership, secure bike facilities, garage parking, and onsite cafeteria. Other benefits include the Employee Assistance Program with up to six face\-to\-face counseling sessions per issue, Global Guardian worldwide assistance, and Business Travel Accident coverage. Additionally, interns working 30 or more hours per week receive holiday pay for 10 observed holidays plus one floating holiday per year and accrue 3\.08 hours of sick leave each pay period with no waiting period. NPR also supports career growth and work\-life balance through professional development opportunities, flexible work hours, and telecommuting options.
Does this sound like you? If so, we want to hear from you.
\#LI\-REMOTE
The range displayed reflects the minimum and maximum salaries NPR expects to provide for new hires for the position across all US locations.
NPR Pay Range
$20 \- $20 USD
NPR is an Equal Opportunity Employer. NPR is committed to being an inclusive workplace that welcomes diverse and unique perspectives, all working toward the same goal – to create a more informed public. Qualified applicants receive consideration for employment without regard to race, color, ethnicity, national origin, ancestry, age, religion, religious belief, sex (including pregnancy, childbirth and related medical conditions, lactation, and reproductive health decisions), sexual orientation, gender, gender identity or expression, transgender status, gender non\-conforming status, intersex status, sexual stereotypes, nationality, citizenship status, personal appearance, marital status, family status, family responsibilities, military status, veteran status, mental and physical disability, medical condition, genetic information, genetic characteristics of yourself or a family member, political views and affiliation, unemployment status, protective order status, status as a victim of domestic violence, sexual assault, or stalking, or any other basis prohibited under applicable law.
If you are a person with a disability needing assistance with the application process, please reach out to [email protected].
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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At National Public Radio, 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
National Public Radio AI Hiring
National Public Radio 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 $173,300 across 2,012 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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