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About This Role
CertainPath \| Reports to: Director of Content \| Remote (occasional on\-site) \| Full\-Time
About the Role
We’re hiring an AI Training Content Specialist to help us modernize and expand our library of training resources — across both video and written content — using AI tools to multiply what our team can ship.
This is a great role for someone earlier in their content career who’s already serious about AI. You’ve been using these tools in your own work and can hold your own editing video and writing copy. The ideal candidate not only understands how to produce content, but also how adults learn — so what you make is clear, engaging, and ready for real\-world application.
The scope here includes refreshing existing training assets, helping our team build new courses, and administering our member site that brings it all together. If you find yourself constantly noticing “we could do this faster with X tool” and then actually doing it, you’ll fit in here. You’ll learn a lot and have a lot of opportunities to stretch your content muscles on a supportive and knowledgeable team.
What You’ll Do
Refresh, Repurpose, and Manage Our Existing Library ( 40%)
- Help refresh existing video content using AI tools — regenerate dated segments with AI avatars and audio, produce Spanish\-language translated and clipped versions with appropriate audio and graphics, and cut long\-form recordings into shorts for various uses.
- Modernize written training resources — update tone and references, refresh examples, generate accurate companion summaries, and build assessments and job aids from existing material.
- Administer the content management system that our member HUB is built on.
Production and Net\-New Content ( 30%)
- Edit fresh video content using AI and standard video\-editing software (Premiere Pro or similar) — long\-form to shorts, training recordings to polished pieces.
- + Produce well\-paced AI avatar explainers and short\-form training videos, using visuals like video, motion effects, and graphics to clearly explain new ideas and concepts to an audience of learners.
- + Create assets — both video and written — that reinforce our training and bring it to life for learners.
- + Assist with video shoots and new written content production as needed.
Support Our AI Workflow Build ( 30%)
- Research, discover, and test new AI tools (video, voice, image, text, agentic) and share honest evaluations with the team — what works, what doesn’t, and what’s worth investing in.
- Help develop and document repeatable AI workflows the broader content team can use and lean on, so refresh\-work and production both move faster over time.
- Help connect platforms (Vimeo, our CMS, AI tools, asset libraries) so handoffs aren’t manual every time.
- Stay current as the AI content stack evolves — and bring what you learn back to the team.
Who You Are
- Solid abilities and instincts in both visual and written craft — you can tell when a training script is muddled or when a video is becoming confusing for the viewer, and you have ideas for improving it.
- An understanding of (or genuine eagerness to learn) how adults learn and what makes training content effective, engaging, and impactful.
- Hands\-on with AI tools across modalities, not just video. You’ve used AI professionally for writing, voice, image, and ideally have started exploring automation or workflow tools.
- Curious about systems and processes — you find yourself thinking “could we build a workflow for this?” not just “let me do it once.”
- Self\-motivated and proactive — you spot opportunities and run with them.
- A proactive and regular communicator — you share your wins, flag your flops, and ask questions when you have them.
- Fluent in the category, not just one brand. Today’s HeyGen or Flow is tomorrow’s something else, and you’re comfortable learning the next thing.
- Organized with strong follow\-through. Nothing falls through the cracks.
- Collaborative and coachable — you welcome feedback and genuinely enjoy being part of a team that’s learning together.
- A positive person who enjoys being part of an experienced team of content creators — instructional designers, video producers, and writers who love what we do, genuinely like each other, and get a thrill from helping others achieve their dreams.
Qualifications
- Demonstrated knowledge and hands\-on experience using AI tools to produce training content (video, voice, and written).
- Video editing skills (Premiere Pro or similar). Comfortable taking long\-form recordings to shorts and full\-length pieces.
- Strong writing and editing instincts for training, marketing, or learning content.
- Hands\-on experience with at least one AI avatar platform (HeyGen, Synthesia, or similar).
- Comfort with file organization, hosting platforms like Vimeo, website CMS, project tracking tools, and spreadsheets.
- An eye for pacing, clarity, and visual storytelling.
Nice to Have
- Instructional design background, or experience producing adult learning content.
- Experience with AI voice tools (e.g., ElevenLabs), AI image generation, prompt engineering, or workflow automation (Zapier, Make, n8n, agentic platforms).
- General video production skills — cameras, lighting, audio, etc.
- Familiarity with the home services or B2B services industry.
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 CertainPath, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
CertainPath AI Hiring
CertainPath has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, 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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