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About This Role
Location
Remote (US)
Employment Type
Intern
Department
Marketing
Compensation
- $24 per hour
At G2, we’re committed to building an equitable and inclusive workplace for all. That's why our compensation program is rooted in market data, transparency, fairness, and individual performance.
Compensation in an offer is determined by factors such as the candidate’s experience, relevant skills, and job-related knowledge.
We’ve built a company-wide framework to provide employees clarity on how compensation and success is measured at each level. This career framework supports our employees' professional growth and guides them to their PEAK.
We also support our employees by offering generous benefits, such as flexible work, ample parental leave, and unlimited PTO. Click here to learn more about our benefits.
About G2 - The Company
G2 is the world's largest and most trusted software marketplace. When you join G2, you’re joining the industry’s leading team that helps businesses reach their peak potential by powering decisions and strategies with trusted insights from real software users.
Now, we have joined forces with Capterra, SoftwareAdvice, and GetApp to create the largest source of online data and software insights to fuel intelligent buying in the age of AI. With 200M+ combined annual visitors and 6M verified reviews, we are now the centralized place to enable software buyers to make better and faster decisions with confidence.
And we are just getting started! We are setting out to transform the global B2B software industry and become the most trusted data foundation for buyers and sellers of software for the age of AI.
Does that sound exciting to you? Come join us as we try to reach our next PEAK!
About G2 - Our People
At G2, everything we are and what we do is grounded in our PEAK values— (*P*erformance + *E*ntrepreneurship + *A*uthenticity + *K*indness. Working at G2 means you are part of a value-driven, growing global community that climbs PEAKs together. We cheer for each other’s successes, learn from our mistakes, and support and lean on one another during challenging times. With ambition and entrepreneurial spirit we push each other to take on challenging work, which will help us all to grow and learn.
You will be part of a global, diverse team of smart, dedicated, and kind individuals - each with unique talents, aspirations, and life experiences. At the heart of our community and culture are our people-led ERGs, which celebrate and highlight the diverse identities of our global team. As an organization, we are intentional about our DEI and philanthropic work (like our G2 Gives program) because it encourages us all to be better people.
About The Role
Summary of Responsibilities:
G2 Marketing is running an org-wide initiative to build AI fluency across every function — from brand and creative to demand gen, content, product marketing, ops, social, SEO, and project management. We've built the framework: a 5-stage adoption model, a validated self-assessment tool, a live results dashboard, and stage-by-stage deep-dives showing teams exactly how to move from individual AI skills to full-scale, cross-team adoption. Now we need someone to help teams actually climb that ladder.
This intern will work directly with marketing leadership to turn the adoption roadmap into measurable progress — helping teams move from experimenting individually with AI to embedding it in daily workflows, and eventually contributing to tools that work across functions. This is a hands-on role, with close support from Marketing leadership.We're looking for someone who's energized rather than unsettled by open-ended problems, who can observe a vague signal ("this team seems stuck") and turn it into a concrete hypothesis and test. Strong candidates will show curiosity and initiative, and evidence of having dug into a problem with genuine interest rather than just executing a process
Detailed Responsibilities:
Roadmap Execution & Deep-Dives
- Recommend and draft Stage 1 deep-dives for remaining marketing functions, following the established starter/advanced prompt-pair and before/after time-savings format, with review and sign off from marketing leadership
- Validate use cases with team leads and get sign-off on deep-dive content
Stage Progression (Stage 1 2)
- Apply the roadmap's diagnostic "you're here when" signals and unlock conditions to identify what's blocking teams from reaching Stage 2, bringing findings to marketing leadership for prioritization
- Work with team leads to support embedding AI workflows into standard practice, not just one-off experimentation
- Track progress through self-assessment score movement and draftcase studies as proof points for leadership review
Cross-Team Enablement (Stage 2 3)
- Support leadership in identifying the team(s) with the highest readiness to advance to stage 3 by analyzing assessment data and surfacing readiness signals
- Contribute to scoping and help execute a cross-team tool or shared workflow with an adjacent function, under the direction of marketing leadership
- Document lessons learned into a repeatable playbook for future teams
Assessment & Measurement
- Support ongoing maintenance of the self-assessment tool and results dashboard
- Help design objective test questions to complement self-reported scores and counteract known self-assessment inflation
Training Support
- Contribute to building out session materials for the 12-session AI fluency curriculum
- Help coordinate rollout logistics and gather feedback for iteration
Qualifications:
Required
- Strong written and verbal communication skills
- Demonstrated interest in AI tools and workflows — coursework, personal projects, or hands-on use count
- Organized, self-directed, and comfortable working across multiple workstreams and keeping projects on track with guidance from a manager
- Analytical mindset — comfortable interpreting assessment data and summarizing insights and recommendations for leadership
- Currently pursuing a degree in marketing, business, communications, or a related field
- Working AI fluency already in hand — regular, hands-on use of tools like ChatGPT, Claude, or similar across different tasks, not just casual or occasional use
- Working familiarity with the AI tools landscape — able to speak to the differences between general-purpose assistants, specialized tools, and emerging agentic workflows
- Eagerness to learn how AI tools apply differently across marketing functions like SEO, creative, and marketing ops, and ability to think concretely about workflow applications
Preferred
- Experience with Google Sheets/Apps Script, Chart.js, or basic data dashboards
- Familiarity with marketing functions (content, demand gen, social, SEO, etc.) and how AI applies differently across them
- Prior internship or project experience involving training, enablement, or cross-functional projects
- Comfort with ambiguity — this role involves building playbooks as much as following them
Our Commitment to Inclusivity and Diversity
At G2, we are committed to creating an inclusive and diverse environment where people of every background can thrive and feel welcome. We consider applicants without regard to race, color, creed, religion, national origin, genetic information, gender identity or expression, sexual orientation, pregnancy, age, or marital, veteran, or physical or mental disability status.
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How We Use AI Technology in Our Hiring Process
G2 incorporates AI-powered technology to enhance our candidate evaluation process. These tools may assist with initial application screening, skills assessment analysis, and identifying candidates whose qualifications align with specific role requirements. While AI technology supports our recruitment workflow, all final hiring decisions remain under human oversight and judgment.
Your Choice Matters: If you would prefer that your application be reviewed without AI assistance, you can opt out by entering your email address in the email entry field at the bottom of the Automated Processing Legal Notice. Choosing to opt out will not disadvantage your application in any way—we will ensure your materials receive a thorough manual review by our hiring team.
For additional details about how we handle your information throughout the application process, please review G2's Applicant Privacy Notice.
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 G2, 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.
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.
G2 AI Hiring
G2 has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $320K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 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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