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About This Role
Fullstack AI Engineer Intern
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Location: ONSITE / IN\-OFFICE Denver
Internship Duration: 6 MONTHS
Department: AI Operations and Engineering
Type: Internship \| Part\-Time \| Paid \| $800\-$1600 per month
Immediate Openings Only: Please note that this internship is for candidates who can interview and start right away. Gesture is NO LONGER accepting future\-dated applications for Summer 2026\. PLEASE DO NOT APPLY UNLESS you are available to begin interviewing asap, and if selected, you can start immediately
About Gesture
Gesture is where technology meets humanity — a place where innovation, emotion, and impact collide. We're a fast\-growing tech company using AI, machine learning, and intelligent logistics to power a first\-of\-its\-kind platform that connects people and brands through real\-world, tangible experiences. From our mobile app to our B2B Reach360 platform, Gesture blends data, emotion, and automation to build the future of human connection — at scale.
Inside our Denver Office, you'll find an environment that moves with the pace and precision of Silicon Valley but with the heart of something far greater.
We run on cutting\-edge tools, creative experimentation, and raw ambition. Every project, every campaign, every moment you work on here matters — because it's seen, felt, and experienced by real people around the world. At Gesture, you'll work alongside some of the smartest, most driven operators, engineers, and creatives in the industry — people who think big, move fast, and care deeply about the work they do.
This is a front\-row seat to the future of connection. If you want to help build something that's changing how the world interacts, welcome to Gesture.
Where We're Headed
Gesture is investing aggressively in AI\-driven intelligence to power the next generation of marketing and commerce using a gift\-centric approach. Our roadmap focuses on:
Building intelligence that anticipates, adapts, and compounds—predicting intent, optimizing campaigns in real time, and delivering deeply personalized experiences driven by real\-world behavior, all through systems that continuously learn and improve with every interaction.
This is not surface\-level automation. We're building intelligence directly into the core of how campaigns are planned, executed, and measured—at scale.
At Gesture, you're not joining a static MarTech company. You're joining a team building the infrastructure for how brands will compete in a post\-digital\-fatigue world.
About the Role
Gesture is building the next evolution of emotionally intelligent consumer engagement. As an Engineering Intern, you will work alongside senior engineers and product leaders to develop AI\-driven tools for our Operations (Marketing, Sales, Fulfillment) team to utilize to optimize and scale our model.
This role is hands\-on. You will contribute production\-level code to systems utilized internally to drive automation, keeping our team working on projects that truly move the needle while learning how engineering, product, and data come together inside a fast\-growing technology company.
What You'll Do
AI Engineering and Personalization Development
- Support the design and implementation of data models, predictive pipelines, and recommendation logic for personalization across the internal Gesture ecosystem.
- Contribute to algorithms that translate user behavior into engagement cycles, rewards, and individualized experience pathways.
Loyalty and Engagement Infrastructure
- Assist in building backend components that power user incentives, behavioral triggers, and reward systems.
- Help develop logic that connects user signals (sending patterns, interaction frequency, referrals, etc.) to loyalty outcomes and campaign eligibility.
Full Stack Feature Development
- Collaborate with the mobile team (React Native) to integrate AI\-driven recommendations, dynamic feeds, and adaptive notification patterns.
- Develop backend services that support A/B testing, targeted campaigns, personalized prompts, and customer journey flows.
Systems Integration and Data Flows
- Work on API integrations between Firebase, Zoho CRM, Segment, Voucherify, and internal data systems.
- Assist in ETL workflows, data ingestion, and ensuring data quality across the ecosystem.
Scalability, Performance, and Quality
- Participate in performance optimization, load testing, and architecture enhancements.
- Learn best practices for building systems designed to scale to millions of users.
Cross\-Functional Collaboration
- Work with product, data science, marketing, and design teams to convert behavioral insights into consumer\-facing features.
Technical Stack Exposure
Frontend
React, React Native, TypeScript and FERN stack
Backend
Python (FastAPI, Express)
Data \& Storage
Firestore, PostgreSQL, BigQuery
Cloud \& Infrastructure
Google Cloud Platform (Firebase, Cloud Functions, Pub/Sub, Cloud Run)
AWS (select services)
Docker, GitHub Actions, CI/CD pipelines
AI / Machine Learning
TensorFlow, PyTorch, scikit\-learn
OpenAI and LangChain frameworks for LLM\-driven personalization
CRM / Marketing Platforms
Twilio Segment, Zoho CRM, Voucherify, OneSignal
Preferred Core Skills and Competencies and Technical Skills
AI / Machine Learning
- Fundamental understanding of predictive modeling and recommender systems
- Familiarity with prompt engineering or LLM APIs
- Interest in behavioral scoring and personalization systems
Full Stack Engineering
- Experience with React or React Native
- Proficiency in backend development with Python
- Exposure to Firebase or GCP services is a strong plus
Data Architecture
- Understanding of API integrations, ETL, and third\-party data flows
DevOps \& Deployment
- Some experience with Docker, testing frameworks, and automated pipelines
- Interest in serverless infrastructure and continuous deployment
Security and Compliance
- Awareness of data handling best practices, including privacy and regulatory considerations
Behavioral Traits
- Customer\-obsessed and motivated by improving user experience
- Strong ownership mentality with the ability to move ideas from concept to production
- Analytical and creative problem\-solving ability
- Rapid learner with curiosity about new AI tools, frameworks, and SDKs
- Emotionally intelligent, understanding the human impact of technology
- Clear communicator who collaborates effectively across teams
Preferred Background
- Pursuing a degree in computer science, engineering, data science, or equivalent technical experience
- Prior work or project\-based experience in full stack development, AI engineering, or mobile app development
- Exposure to personalization engines, loyalty systems, or recommendation algorithms is a plus
- Startup or fast\-paced engineering environment experience is beneficial
What You'll Gain at Gesture
Meaningful, Shippable Work
Every intern contributes to features that go live in the product. No shadow projects.
Professional Development and Mentorship
- Direct mentorship from senior engineers
- Biweekly sessions with executives and division leads
- Structured growth guidance and portfolio development
Leadership and Strategy Exposure
- Invitations to the Founders' Roundtable where strategy, scaling, and fundraising are discussed
- Opportunities to observe how product and technical decisions are made at leadership level
AI Innovation Environment
- Access to Gesture's internal AI Playground for prototyping and experimentation
- Guided learning on how AI integrates with product, logistics, and consumer experience
Application Process \- Interested candidates should submit the following
A resume outlining relevant experience.
A brief cover letter highlighting your key skills, achievements, and what excites you about this opportunity.
Any project work, portfolio samples, or coursework that demonstrate your passion and abilities.
Download The Gesture App
*We strongly encourage all applicants to download the Gesture app and explore our platform firsthand (https://sendagesture.app.link/breezy2025\). You can also visit gesture.vip to learn more about our mission, technology, and culture.*
Culture
At Gesture, we operate with a KPI\-driven, pacesetting culture — one that rewards sharp, motivated, high\-performing individuals who take initiative and execute with excellence.
The ideal candidate will thrive in a startup environment, embodying curiosity, grit, and a builder's mindset. This is a ground\-floor opportunity to grow alongside one of the most exciting and innovative companies redefining connection through technology.
Apply today to join Gesture and be part of the team that's redefining how the world connects.
Join the Movement ! If you're ready to lead by doing, grow fast, and make an impact that matters — we want to hear from you.
Salary Context
This $9K-$19K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At GESTURE, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($14K) sits 91% below the category median. Disclosed range: $9K to $19K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
GESTURE AI Hiring
GESTURE has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, New York, NY, US. Compensation range: $19K - $150K.
Location Context
AI roles in Denver pay a median of $198,000 across 169 tracked positions. That's 8% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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