Interested in this AI/ML Engineer role at Eulerity?
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About This Role
As a Marketing Intern at Eulerity, you'll get a real look at how our marketing team works, from content and brand work all the way through the automation and data that drives demand generation. You'll pitch in across content, campaigns, and operations, and you'll actually own projects, not just watch from the sidelines. You'll also get to use AI tools as part of your everyday work.
Core Responsibilities
- Shoot and edit video content across customer testimonials, social clips, and website assets, you're comfortable holding a camera and getting a good shot, then taking that footage through editing and adding animation/motion graphics to make it publish-ready.
- Help write and schedule LinkedIn posts for our marketing and sales teams, covering conferences, other initiatives, and engagement content.
- Help out with our podcast: researching guests, prepping talking points, and handling episode logistics.
- Help keep our website updated, put together our newsletters each cycle, and pitch in ideas for an upcoming site revamp.
- Assist with research and first drafts for industry insight reports, white papers, and blog posts, using AI tools to help speed up the drafting.
- Help build outbound email sequences that improve click rates, open rates, engagement, and lead volume.
- Research industry verticals like Health & Wellness, Fitness, Home Improvement, Real Estate, and Tutoring to help shape our messaging.
- Help build renewal and upsell materials that support Customer Success.
- Help track campaign performance and pull together marketing metrics reports.
- Help keep our lead scoring and attribution data clean and accurate.
- Learn your way around automation tools like n8n, Zapier, or even Claude agents like Claude Code, and help build workflows in them.
About Eulerity
Eulerity is an AI marketing automation platform that makes digital advertising simple for businesses of all sizes, from local franchises to global enterprises. As a Marketing Intern, you'll work closely with the team shaping how we go to market, with a front-row seat to how a growing B2B SaaS company builds its brand, pipeline, and marketing operations.
Requirements
- Currently pursuing a degree in Marketing, Business, Communications, Engineering, or something related and graduating no earlier than May 2027.
- Hands-on video skills: comfortable shooting footage on your phone or a camera, editing it into a finished piece (CapCut, Premiere, Final Cut, or similar), and adding basic animations or motion graphics when the content calls for it.
- Strong writing skills, and you're comfortable writing in different formats, from social posts to emails to longer articles.
- Genuine interest in AI, marketing technology, or ad-tech. Bonus points if you're excited to learn about multi-location marketing and franchise businesses.
- A generalist mindset. You like wearing multiple hats, you're comfortable with ambiguity, and you thrive in a fast-paced environment.
- It's a plus if you're familiar with HubSpot or another CRM, have used automation tools like n8n, Zapier, or even Claude agents like Claude Code, or have managed social media accounts before.
- Also a plus: basic design skills and some comfort with tools like Canva, Figma, or similar tools.
What You'll Gain
Beyond the day-to-day work, you'll get:
- Direct mentorship from the marketing team and regular feedback on your work.
- Exposure to the full marketing funnel, from content and brand work all the way to demand gen and marketing operations.
- Hands-on experience using AI and automation tools in a real business setting.
- Room to grow your skills across content, campaigns, and data, with real ownership as you go.
Logistics
- 20-30 hours per week, flexible schedule.
- $18 to $20 per hour.
- Lunch credits and unlimited snacks when in-office.
- Direct mentorship from senior operators on our team.
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Salary Context
This $37K-$41K 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 Eulerity, 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 ($39K) sits 81% below the category median. Disclosed range: $37K to $41K.
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.
Eulerity AI Hiring
Eulerity has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Jersey City, NJ, US, New York, NY, US. Compensation range: $41K - $41K.
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.
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