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About This Role
### Full\-time • Remote
Introduction
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You already live inside Reels, TikTok, and Shorts. You know what it takes to keep someone watching past three seconds, and you’ve started using AI tools to move faster, auto\-captions, AI B\-roll, voice clones, the works. Native Commerce is looking for an AI Video Editor to turn talking\-head and screen\-recorded content into high\-performing short\-form videos for creators and brands.
Team Mission
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Our Social team turns raw ideas into content that actually moves numbers, views, leads, and revenue. You’ll sit at the intersection of creative, AI, and performance, building a repeatable pipeline from long\-form footage to short\-form clips that consistently earn attention.
Key Responsibilities
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- Edit short\-form social content (TikTok, Reels, YouTube Shorts) with strong hooks, clean pacing, and a mobile\-first layout.
- Use AI platforms (Higgsfield, Midjourney, HeyGen, ElevenLabs, etc.) to generate avatars, voice\-overs, B\-roll, and variations that speed up delivery.
- Turn long\-form content (podcasts, YouTube videos, webinars) into multiple clips, teasers, and highlight reels that maximise each recording.
- Add subtitles, on\-screen text, pattern interrupts, and sound design to boost retention and watch\-through.
- Use performance data (retention graphs, drop\-off points, completion rates) to decide what to cut, what to move, and what to test next.
- Collaborate with the team to translate simple briefs into platform\-native edits that match brand tone and campaign goals.
- Stay on top of short\-form formats, meme styles, and editing trends; propose new concepts and tests, not just “wait for tasks.”
- Keep your projects organised, clear file naming, version control, asset libraries, and exports ready for each platform.
Must\-Have Skills
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- 3\+ years editing short\-form, social\-first content or a portfolio that clearly shows high\-output, high\-quality work.
- Strong skills in a pro editor (Capcut, Premiere Pro, After Effects, or similar) and hands\-on experience with AI media tools (for avatars, VO, generative visuals, or AI video).
- Experience working with creators, personal brands, or performance\-focused teams (agency, DTC, info products, coaching, etc.).
- Solid understanding of short\-form retention, how hooks, pacing, framing, and captions impact viewer behaviour.
- Proven ability to repurpose long\-form content into multiple short assets with minimal supervision.
- Comfortable working to tight deadlines, handling feedback loops, and shipping consistently.
Nice\-to\-Haves
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- Background in performance marketing or creator\-commerce; understands how creative connects to offers, funnels, and revenue.
- Audio design and color\-grading and color correcting skills to level up overall polish.
- Experience prompting/scripting generative\-AI tools (Midjourney, DALL·E, Runway or similar) to create supporting visuals.
- Previous experience in a remote, async team using shared libraries and project\-management tools.
Compensation \& Benefits
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- Competitive salary pegged to top\-quartile market rates
- Flexible schedule (core overlap 2 hours with GMT\+8\).
About Native Commerce
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Native Commerce fuels 16 niche brands and six newsletters, reaching 30 million\+ readers every month with fast, useful content that turns passion into action.
Native Commerce Culture
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Curiosity drives everything we do. Our work rests on clear promises: Know the Reader, Start Small, Learn Fast, Create with AI, Craft with Intention, Track What Moves the Needle, Be Candid, Be Kind. Together, these keep us building fast, improving constantly, and supporting each other along the way.
We’re a remote\-native team across time zones, so clarity and trust matter. Check\-ins help us stay aligned, lean into our strengths, and move through feedback quickly, direct enough to improve the work, thoughtful enough to build the person.
And when something works? The Slack threads light up and the virtual gong gets a workout. Progress fuels momentum, and momentum builds the next idea. If you value honest collaboration, smart metrics, and a culture that celebrates both wins and growth—you’ll feel right at home here.
If this role sparks your curiosity, even if you don’t meet every requirement, we want to hear from you. Apply and tell us how you’d grow an audience into a thriving revenue engine
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Native Commerce, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Native Commerce AI Hiring
Native Commerce has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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