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About This Role
Company Introduction
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WorldStrides is the global leader in educational travel and experiential learning. The company was founded in 1967 to provide middle school travel programs to Washington, D.C. and has grown to provide a wide range of programs for more than half a million students annually to over 100 countries around the world. WorldStrides offers experiential learning programs in educational travel, performing arts, language immersion, career exploration, service\-learning, study abroad, and sports. Each of these experiences helps students to see beyond the classroom and to see the world – and themselves – in new ways.
Job Description:
A hybrid builder who finds the highest\-leverage problems across WorldStrides, prototypes working solutions with AI\-assisted development, and hands them off to engineering for enterprise scale.
Product Engineers are part product manager, part developer, and part solutions architect. They embed with teams across the business — operations, sales, marketing, finance, customer support, academics, and beyond — find where the company can create value, and then build it. That value might be a brand\-new customer experience, a new digital capability, a smarter platform feature, or a redesigned internal process. They own the outcome, not the ticket.
Product Engineers deploy wherever the leverage is. The work is the same wherever it lands: get close to the real problem, find the leverage, build the proof, and graduate it to production with engineering.
*Discovery — find the leverage.*
- Embed with stakeholders across lines of business to understand how work actually gets done and where the real opportunity is — whether that's an unmet customer need, a missing capability, or a broken workflow.
- Identify and frame opportunities, then qualify them: is the problem worth solving — material, real, measurable, and feasible to build?
- Build the case for the work — quantify the prize, whether that's revenue or growth, a better customer experience, or hard savings, cost avoidance, and capacity released — and pressure\-test it with finance and product partners before committing the team's time.
- Design and build both to support proof\-of\-concept and pilot — working software, not slideware.
- Use AI\-assisted development to move fast: speed and learning matter more than polish at the POC stage.
- Connect prototypes to real systems of record (i.e., Dataverse, in\-house systems) through approved integration patterns, reading and writing only via sanctioned product\-owner requests.
- Run pilots with real users, instrument outcomes, and iterate against evidence.
*Hand off — land the plane with IT, not around it.*
- Build to be portable from day one: clean, reviewable code that engineering can adopt rather than rebuild.
- Partner with the IT organization to graduate prototypes to production, and leverage org\-wide DevOps and SDLC (trunk\-based development, PR and peer review, CI/CD, observability) as those capabilities mature.
- Document architecture, decisions, and assumptions so the work survives the handoff.
*Feed the system.*
- Every project is a live R\&D loop. Surface the patterns, edge cases, and insights you find so they shape the broader product and platform roadmap.
- Partner effectively across multiple business areas and levels of leadership to identify, appreciate, and leverage varying perspectives, ways of working, and priorities
Requirements:
- Strong customer focus and insights\-driven prioritization. You care deeply about delivering solutions that create real customer value, support business goals, and compound the leverage of our technologies and operations.
- 1\-3 years building software, products, or automations in a role where you owned outcomes — not just executed assigned tickets. (Strong new\-grad candidates with a demonstrable portfolio of or substantive assignments will be considered for the entry level.)
- Demonstrated ability to build full\-stack: you can stand up a working front end and wire it to back\-end logic and data.
- Evidence you can run discovery with stakeholders or users — you've talked to real people, understood their need or their problem, and translated it into something you built. Say so explicitly, with the outcome.
- AI\-assisted development fluency: you build *with* AI tools (coding agents, LLM application patterns), not around them. We care that you can learn new AI tooling fast and that you're staying up\-to\-date on the latest advancements — far more than which specific tools you've used before.
- Clear written and verbal communication. You can explain a tradeoff to a business leader and a data model to an engineer in the same afternoon.
- Bias toward shipping. You'd rather have a rough working prototype in front of a user this week than a perfect plan next month.
*Strongly preferred*
- Experience integrating applications with enterprise systems of record via APIs.
- Familiarity with the Microsoft ecosystem (Azure, Azure DevOps, Dynamics/Dataverse, M365\) or the ability to ramp into it quickly.
- Exposure to LLM application development — prompt design, retrieval\-augmented patterns, agent/tool\-use chains, and especially evaluation (building checks that catch hallucinations and regressions before they reach a pilot).
- Comfort working inside messy, real\-world business constraints — legacy workflows, fragmented data, security and compliance gates.
Qualifications:
*Technical*
- Front\-end: React, modern JS/TypeScript, responsive UI.
- Back\-end \& data: API design and integration, SQL, relational data modeling; comfort reading/writing to systems of record under governance.
- AI\-assisted build: coding agents and AI development tools; LLM application patterns including prompting, RAG, tool/agent orchestration, and eval design.
- Engineering hygiene: version control and trunk\-based development, pull requests and peer review, secrets kept out of source control, SSO/AD\-based identity. Familiarity with CI/CD and observability is a plus.
- Our environment is Microsoft\-ecosystem aligned, including SSO/AD for auth, Azure DevOps Git for source control, Dynamics Dataverse, and .NET as the production handoff languages.
*Product*
- Discovery and problem framing: user and stakeholder research, opportunity identification, getting to the real need behind the ask.
- Distillation of complex experiences and workflows, including identification of when to simplify or eliminate elements while maintaining or improving outcomes.
- Opportunity qualification and prioritization against a clear value framework.
- Value modeling: sizing impact credibly enough to defend with finance.
- Outcome ownership: defining success metrics and holding to them after launch.
*Human*
- Stakeholder empathy and the ability to earn trust quickly inside teams you don't belong to.
- Radical ownership — you treat the problem as yours end to end.
- Adaptability — the tools and AI capabilities will change under you, repeatedly. You stay ahead of that.
- Handoff discipline — you measure success by what reaches production and gets adopted, not by what you personally built.
Work Perks
- Fun \& driven environment.
- Excellent medical, dental, and vision coverage, life, accidental death and dismemberment, accident, critical illness, and disability insurance, FSA healthcare, FSA dependent care, HSA with employer contribution, and generous 401k match.
- 11 paid floating corporate holidays, 1 paid volunteer day \& up to 25 PTO days to start – accrue up to 28 over 3 years, 4 mental health days, and 5 bereavement days.
- Tuition reimbursement up to $5,250 annually
- $1,000 towards professional certifications annually
- Opportunities for paid and discounted travel.
- Flexible work schedule providing on\-site, remote, and virtual office opportunities.
- Encouraged participation in our Employee Resource Groups and Diversity, Equity, \& Inclusion council.
- Fitness Center and café onsite at select locations.
- Employee Assistance Program (EAP)
- Paid Parental, Caregiver, and Disability leave.
- Team Member Discount Program
*WorldStrides, a global organization, is committed to* *educate and serve communities worldwide. Our commitment is fueled by the passion of our team members and partners to make experiential learning accessible, while also being socially, environmentally, and ethically responsible. Together, we accomplish this by investing in initiatives to promote inclusion, diversity, and sustainability.*
*As an Equal Opportunity Employer, WorldStrides is committed to building a diverse workforce, supported by an environment that promotes inclusion and belonging. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.*
*WorldStrides will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas such as E, F\-1, J\-1, H\-1, H\-2, L, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible for hire. Select seasonal roles may consider students on J\-1 or F\-1 visas.*
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 WorldStrides, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
WorldStrides AI Hiring
WorldStrides has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlottesville, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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