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About This Role
### About Single Grain LLC
Single Grain is a Revenue Marketing agency. We build pipeline\-focused systems that compound — not campaigns that expire. We've helped 500\+ companies, from venture\-backed startups to the Fortune 500, drive measurable growth, influencing billions in revenue with a 3\.2x average ROI.
Our portfolio includes:
- Single Grain Agency — enterprise digital marketing services across SEO, paid media, creative, CRO, and AI\-driven growth strategy.
- ClickFlow — our AI\-powered content optimization platform that helps companies unlock hidden growth from their existing content by identifying high\-impact SEO opportunities and improving rankings with AI insights.
- Karrot — a LinkedIn advertising and landing page platform built specifically for B2B companies running demand generation programs.
- Single Brain — the complete client operating layer: one agent interface for the human, one agent fleet commander for execution, an agent fleet of worker operators, and vertical specialists that handle the actual work.
We are evolving into a tech\-enabled services (TES) company, where software, automation, and services combine to create leverage. Our CEO Eric Siu hosts the Leveling Up podcast and co\-hosts Marketing School with Neil Patel, reaching millions of marketers and founders every month — a brand ecosystem most agencies don't have.
We are fully remote, and we operate as an AI\-native team: every role at Single Grain is expected to use AI to increase speed, quality, and leverage.
### About the Role
Single Grain's creative has to move at the speed of the AI era, and the last thing we need is another specialist who only does one thing. This seat is built for a flexible, AI\-empowered creative who moves fluidly between disciplines — motion, UGC\-style video, static design, editing, and the strategy and copy behind them — and uses AI to combine them in one person's hands.
You take an idea from concept to a finished, in\-market asset. One day that's a batch of motion ads, the next it's UGC edits and a set of statics, the next it's a script and a creative angle for a launch. You use AI workflows to cover ground that used to take a whole team, and you finish by hand where craft demands it. You build deep knowledge of our clients — their brands, their voice, what converts for them — so the work lands without a long brief. As the tools change month to month, you change with them.
### What You'll Own
- Take ideas from concept to finished asset across motion, UGC\-style video, short clips, and static, flexing to whatever the week needs.
- Contribute to strategy and copy, not just execution: sharpen the angle, write the script, shape the message.
- Build and maintain AI\-assisted workflows that let one person cover ground that used to take a team, without lowering the quality bar.
- Turn a single brief into a multi\-format creative set (video plus static variants) ready for paid testing.
- Develop deep knowledge of our clients' brands, voice, and what converts, and bring it to every asset.
- Keep several client accounts supplied with fresh creative on campaign and event deadlines, prioritizing across them.
- Partner with paid media on what's fatiguing and what's winning, and turn it into the next round of variants.
- Evaluate new AI creative tools as they ship and fold the useful ones into the workflow.
### Hard Skills You'll Bring
- Genuine range across motion and motion graphics (After Effects, Premiere), video editing, and static design (Adobe Creative Suite, Canva) — multi\-disciplinary, not a specialist who dabbles.
- Short\-form and UGC\-style video editing for social and paid — cutting creator, stock, and AI\-generated footage into finished ads.
- Scriptwriting and a feel for strategy and copy — you can shape the message, not just lay it out.
- Working fluency with generative AI creative tools — AI video (Runway, Kling, Sora, Veo) and AI image (Midjourney, Gemini / nano\-banana) — and the judgment to edit their output.
- At least a working base of Claude Code, and comfort building AI\-assisted workflows.
### You May Be a Good Fit If
You bring 3\+ years in motion\-forward creative production, with a portfolio and reel that show range across formats.
- You are flexible by default — you would rather cover design, video, and strategy than be boxed into one.
- You treat AI as the thing that lets you combine those disciplines in one seat, not a gimmick.
- You are relentlessly self\-upgrading: when a new tool ships, you have already tried it by the time it comes up in a meeting.
- You hold a craft bar and know when AI output is good enough and when it needs your hands on it.
- You can shape an angle and write a tight script, not just execute someone else's brief.
- You move fast across several accounts and learn a client's brand deeply enough to work without a long brief.
- You share what you learn; your prompts and workflows make other people faster.
- Bonus: you can design and build landing pages (HubSpot) for message\-matched post\-click experiences.
### Cross\-Functional Partners
You will report to the Creative Lead and work day\-to\-day with the paid media team, who run your creative in\-market and bring back what is converting. You will coordinate with client\-side production teams to get spokesperson and event footage shot to your scripts. You will mostly work through the account leads, with occasional direct client contact to present or align on creative.
### What This Role Is Not
- Not for a one\-sided specialist. If you only design, only edit, only strategize, or only prompt, this is not the seat.
- Not purely manual and not purely AI. You need both — AI to move fast, hands to finish.
- Not a role that waits for a full brief. You learn the client and bring the idea.
- Not a single\-account role. You keep several clients fed at once and prioritize across them.
- Not a set\-and\-forget toolset. The tools change constantly, and so must you.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Single Grain, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Single Grain AI Hiring
Single Grain has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US.
Location Context
AI roles in Los Angeles pay a median of $215,000 across 397 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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