Interested in this AI/ML Engineer role at Storm Ideas?
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WHY US?/
USD 18,000 to USD 22,000 per year, depending on experience, on a full time, contractor contract
Fully remote working!
✨ Working with globally recognisable brands in US Entertainment
Work with modern hardware, social publishing tools, productivity systems, and AI-enabled workflows.
Who We Are/
Who We Are
Founded in 2008, Storm Ideas is an international team working with globally recognised clients, particularly across the US TV and entertainment industry.
Our work sits at the intersection of media, technology, creativity, and operations. We produce social content at scale, support major entertainment brands, build products, and keep improving how our teams work.
We are remote-first, fast-moving, and increasingly AI-native in how we operate. AI tools and agentic workflows are becoming part of the way our teams plan, check, coordinate, research, report, and improve work. The standard is not “let AI do it”. The standard is better work: clearer briefs, faster execution, sharper checks, stronger judgment, and repeatable workflows that keep quality high.
About The Role
We are hiring an AI Social Media Coordinator to help run the day-to-day social presence of major US TV and entertainment brands.
The craft is familiar: scheduling, reviewing, coordinating, optimising, checking, escalating, and keeping content moving. How we do that work is changing fast.
This is not a pure creative content role and it is not passive admin. The core of the job is client-safe social media operations: making sure content is scheduled, checked, approved, published, documented, and improved with strong attention to detail.
You will first learn what good looks like for our clients and why it matters. Then you will work with Storm’s social tools, workflow systems, and AI-enabled ways of working to help make the role faster, clearer, and more reliable.
The strongest candidates will already be practical and curious about AI. We are not looking for someone who uses AI only as a search box or autocomplete. We are looking for someone who can brief tools carefully, use them for bounded pieces of work, review what comes back with a critical eye, and keep improving how they use them.
If that already feels like how you work, this role is likely to suit you.
What You’ll Make Happen/
- Put the client experience and publishing accuracy at the heart of everything you do.
- Optimise YouTube content with effective titles, thumbnails, copy, metadata, and publishing checks.
- Schedule, review, and coordinate social content across YouTube channels and social networks.
- Support day-to-day community management across client social channels by monitoring and responding to audience comments and messages in line with agreed brand guidance, and escalating sensitive or complex interactions appropriately.
- Clear dependencies proactively, including assets, copy, captions, approvals, account access, platform requirements, timings, and client expectations.
- Coordinate with client contacts and Storm’s content production, copywriting, captioning, and social teams so work moves without avoidable gaps or surprises.
- Use AI tools and workflow systems to support repeatable coordination work, including calendar planning, dependency chasing, content checks, research, account audits, reporting, and documentation.
- Review AI-assisted work critically before it reaches clients, platforms, or internal stakeholders.
- Research clients’ shows, movies, channels, competitors, and audiences so social recommendations are grounded in useful context.
- Monitor social platform updates and identify practical changes that matter for client accounts.
- Help document and improve internal processes so that good ways of working become repeatable across the team.
- Escalate clearly when something is blocked, risky, unclear, late, or outside your authority, bringing evidence and a sensible next step.
YOUR EXPERTISE/
- Hands-on social media coordination, publishing, community, content operations, or adjacent digital experience.
- Practical AI fluency: you have used AI tools in real work, study, or personal projects, and can explain how you check their output.
- Strong attention to detail, especially around copy, timing, assets, approvals, account selection, links, platform requirements, and final pre-publish checks.
- Clear written communication and the ability to explain what is done, what is blocked, what is risky, and what needs a decision.
- Strong organisation and workload management across multiple accounts, deadlines, and stakeholders.
- Comfort using social publishing tools, spreadsheets, project management systems, documentation, and other workflow tools.
- A systems mindset: you see repeated work as something that can be made clearer, safer, and more efficient.
- Curiosity about social platforms, audience behaviour, entertainment brands, and the changing AI tooling landscape.
- Sound judgment about when to act independently, when to ask, when to escalate, and when not to publish.
- Ability to analyse information qualitatively and quantitatively, then turn it into useful recommendations.
- High agency: you do not wait passively for perfect instructions when there is a sensible next step to take.
What Success Looks Like
- Content goes out on the right account, at the right time, with the right assets, copy, metadata, approvals, and platform checks.
- Client contacts and internal teams trust you because your updates are clear, accurate, and useful.
- You catch issues early: missing assets, unclear approvals, broken links, platform constraints, scheduling conflicts, weak copy, mismatched account details, or risky assumptions.
- You use AI and workflow tools to make repeatable work faster and more reliable, without handing over judgment.
- You improve the operating system around the work: checklists, documentation, calendar views, QA steps, escalation routes, reporting, and lessons learned.
- You distinguish urgent execution from optional strategy, and you know how to keep both moving at the right level.
- You make the team calmer and more effective because the details are visible, controlled, and followed through.
This Role Will Suit You If
- You enjoy the operational side of social media: coordination, publishing, QA, process, timing, and follow-through.
- You like using modern tools to make repeated work more reliable.
- You care about accuracy and client trust as much as you care about ideas.
- You are comfortable working across multiple accounts and deadlines without losing detail.
- You want to work in an AI-native team and are ready to bring those methods into everyday social media coordination.
- You can take feedback, improve your process, and keep raising your own standard.
This Role Will Not Suit You If
- You mainly want a creative strategy role with little day-to-day coordination.
- You are uncomfortable with detailed checks, repeated processes, or publishing accountability.
- You treat AI-generated output as finished work without reviewing it.
- You wait for someone else to notice problems that you could reasonably have surfaced.
- You prefer broad social media opinions over client-safe execution and evidence-backed recommendations.
What It’s Like To Work Here/
Modern Tools And Working Methods
Work with current hardware, social tools, productivity systems, and a properly supported AI stack. You will be expected to learn the tools, use them thoughtfully, and help improve how the work gets done.
Agility And Openness For Ideas
We actively encourage people to bring forward ideas that improve how teams work. We value practical experimentation, quick learning, and clear follow-through.
Client And Audience Impact
Our work supports major entertainment brands. Small details can matter: timing, approvals, links, copy, thumbnails, account selection, and platform rules all affect trust.
Remote-First Working
This role is fully remote. Your normal workday will start at 9:30am Pacific Time.
Recruitment process/
- First, you will receive questions to answer via a self-recorded video.
- If successful, we will invite you to an interview (60 minutes via MS Teams). During the meeting, Bella, our Head of Social Media Management, will get to know you and try to understand if you’d be a good fit for the role, and at the same time answer all your questions.
- Following this, there will be a short task to test your attention to detail and communication skills.
- There may also be a short (30 minutes via MS Teams) follow-up interview with Duncan, our Chief Operating Officer.
- And that’s it!
Salary Context
This $18K-$22K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Storm Ideas, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($20K) sits 91% below the category median. Disclosed range: $18K to $22K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Storm Ideas AI Hiring
Storm Ideas has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $22K - $22K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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