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About This Role
Our Vision \& Products
==========================
*EverAI — Building the Future of AI Companionship*
*One of the Top 15 Largest \& Fastest\-Growing AI Companies in the World*
*50 Million Users in 2 years — Help Us Reach 100M first, 500M next*
At EverAI, we’re shaping what it means to connect with AI. With 50 million users and counting, we're not just building products — we're creating entirely new categories.
Our flagship product is the world’s largest AI companionship platform, redefining relationships for millions. It is governed by our proprietary moderation system, EverGuard — an internal AI designed to ensure everything we build is safe, ethical, and human\-first.
*And we’re only just getting started!*
Our Team
============
We are an enthusiastic, passionate and hardworking team of 75 people. Our founding team has strong entrepreneurial experience building and scaling web products from 0 to IPO.
Alexis Soulopoulos \[CEO]
- *10\+ years in Tech Executive Leadership*
- *Co\-Founder Mad Paws Holdings (from 0 to IPO)*
- *Forbes 30 under 30 \+ Deloitte TechFast50 ’22 \& ‘23*
Michael Monin \[Co\-founder \& CTO]
- *10\+ years as CTO / COO (web2/web3\), 1\+ year in AI/LLM*
- *Serial\-entrepreneur: MTK Digital (exited / 0\-\>$20m revenue) and Zipchat (AI Chatbot for E\-commerce brands)*
Thomas Lacroix \[Co\-founder \& CMO]
- *8\+ years in Customer Acquisition \& E\-commerce Growth*
- *Serial\-entrepreneur: Curatible (sold to Blackstone) and MTK Digital (exited / 0\-\>$20m revenue)*
Maruša Fasano \[CFO/Legal]
- *25\+ years in Finance, Strategy, M\&A*
- *Ex\-CFO/M\&A @Curatible (exited to Blackstone)*
- *Ex\-President of the Board @SotremoSA (exited)*
- *Co\-founder/CFO @SoftOne (exited)*
Your Role
=============
We're hiring a full\-time senior director to own the end\-to\-end production of a vertical (9:16\) short drama series. This is NOT a generic "AI video creator" role. We need experienced storytellers, specialized in mini dramas, who can be taught our AI stack.
Key Responsibilities
========================
- Own 1–2 series at a time (10–15 episodes per series, \~1 min per episode \+ a trailer).
- Work with our in\-house AI copilot for character consistency, casting, and trailer generation.
- Direct, edit, and finish each episode yourself. You create scripts; you bring the storytelling and craft.
- We will teach you AI Content creation. If you have a strong background in film editing, you will learn fast.
- Collaborate with our team of AI Content Creators, tech team, other Short Film Directors, and our exec team
Your Qualifications
=======================
Hard Skills
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- 4\+ years directing or showrunning short\-form narrative video.
- Strong scripting instincts: hooks, cliffhangers, minute\-to\-minute escalation.
- Have played around with AI generation tools such as Runway Gen\-4, Sora 2, Kling 3, Veo 3, Pika, Higgsfield, and Seedance. We will teach you to become an expert quickly.
- Experience with vertical drama platforms.
Soft Skills
---------------
Strong communication \& collaborative skills (perfectly fluent in English)
Goal\-oriented, ownership and commitment
️ Doer mindset \- we are moving fast and need people who can balance execution, planning, and strategy
Obsessive about speed, performance and iteration
Humble \- willing to learn, open to feedback
Why EverAI?
===============
Exponential Growth: From 50M users in 2 years, to 100M next — and 500M beyond
Track Record of Category\-Creating Innovation: We consistently launch world\-first AI applications — setting the pace, not following it
Global Impact: Top\-tier user growth, real\-world adoption, and cultural relevance
Proven Leadership: A senior team that’s launched, scaled, and exited \& IPO’d multiple scale ups — now fully focused on reshaping AI companionship
Elite Remote Team: 100% remote and built to win — world\-class talent from Tier 1 tech companies, with a culture of ownership, velocity, and radical creativity
Ethical Core: Our AI ecosystem is governed by EverGuard, our proprietary AI moderation technology, ensuring responsible development at scale
What We Offer
=================
Contract Type: We prefer B2B, but we’re flexible, what matters is long\-term commitment and impact
Work From Anywhere: Fully remote. Choose the environment where you do your best work
Paid Time Off: 4 weeks (20 working days) of PTO per year to recharge and reset
Annual Gathering: A yearly in\-person meetup to connect, brainstorm, and celebrate wins together
Health \& Wellness Support: Monthly allowance of100 USD for health insurance expenses \+ unlimited 1:1 sessions with psychologists and lifestyle experts through *OpenUp* (also available for up to three family members)
Co\-Working Space Budget: Work from a co\-working space up to twice per month (35 EUR / 40 USD per visit) to stay inspired and connected
Learning Budget: Dedicated funds to support your professional growth: courses, books, conferences, events, or certifications
Equipment: Company laptop provided \+ monitor budget up to 250 USD for your workspace setup
AI Tools Access: Premium access to ChatGPT, Cursor, Hugging Face, Claude Code, and any other tool needed to excel at your job, power your ideas and workflows
Top Tier Talent Is Our Multiplier
=====================================
We’re a fully remote group of A\-players from Tier 1 tech, led by an exec team who’ve launched, scaled, and exited multiple companies. We move fast, and care deeply about what we build — and who we build it with.
We’re looking for exceptional talent ready to ship \& distribute world\-first AI products at scale, fast, and co\-create with us this category\-defining business.
If that’s you — reach out and apply!
External Referral Program
-----------------------------
Know someone who could be a great fit for this role? You can refer them through the EverAI External Referral Program and earn a bonus of up to 2,500 USD if they’re hired. Submit a referral here.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EverAI, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EverAI AI Hiring
EverAI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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