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About This Role
Please review this posting in its entirety, as we are seeking a very specific candidate profile.
To be considered, applicants must have professional one\-on\-one mindset coaching experience along with demonstrated, hands\-on use of AI tools. Athletic, nutrition, or fitness coaching backgrounds alone will not meet the requirements for this role.
This is a high\-level professional coaching position that requires a serious commitment of a minimum of two years.
Join Our Elite International Coaching Team
Amplify AI Coaching is expanding our global team of exceptional coaches.
We partner one\-on\-one with high\-level clients to help them achieve greater clarity, purpose, engagement, and fulfillment across career, health and energy, relationships, and personal freedom.
Our clients include Fortune 500 CEOs, NASA scientists, and Grammy Award\-winning artists.
This is one of the most elite, results\-driven AI\-integrated coaching programs in the industry today.
If you are an experienced professional coach who delivers transformational one\-on\-one coaching and understands how to leverage AI to amplify results, this is a rare opportunity to work alongside a high\-caliber team that creates meaningful impact every day.
As part of our interview process, candidates will be required to submit a recorded coaching session for review, as well as a separate AI demonstration showcasing their ability to confidently use AI tools in a practical business or coaching scenario.
Position Overview
This is a serious contractor coaching role. Upon successful completion of initial paid training, you will be entrusted with international clients.
There is no marketing or sales required. Your sole focus is delivering exceptional coaching outcomes.
You are compensated per session and maintain control over your schedule.
Important expectations:
- This role requires a minimum two\-year commitment
- Training and ramp\-up take time, this is not a quick\-start role
- We will not consider candidates currently in a full\-time position
- Ideal candidates are already coaching their own clients or working part\-time in other professional capacities
Core Responsibilities
As a coach, you will be expected to:
- Teach clients how to use AI tools in simple, approachable ways
- Build small, personalized workflows that improve productivity and efficiency
- Demonstrate how AI supports decision\-making, planning, research, communication, and ideation
- Create prompts, templates, and client\-ready AI tools
- Translate technical concepts into clear, everyday language
- Support clients who feel overwhelmed or resistant to adopting AI
- Guide clients in building sustainable habits and integrating AI responsibly
- Ensure all AI usage aligns with ethical and safe best practices
Required Experience and Qualifications
- 2 to 5\+ years of professional one\-on\-one mindset or performance coaching experience
- Experience coaching executives, entrepreneurs, or high\-level professionals
- Strong, demonstrable experience using AI tools in real\-world applications
- Proficiency with platforms such as ChatGPT, Claude, or similar tools
- Ability to clearly explain and apply AI in practical, client\-focused ways
- Excellent communication and writing skills
- Highly organized, disciplined, and reliable
- Tech\-savvy, curious, and adaptable mindset
Preferred:
- Master\-level coaching certification (ICF MCC or equivalent experience)
Schedule Expectations
- Availability Monday through Friday
- Ability to coach a minimum of 15 sessions weekly (approximately 20 to 22 hours)
- Full\-time availability is preferred, but not required
Benefits of Working with Amplify AI Coaching
- Fully remote, coach from anywhere
- Flexible schedule that supports work\-life balance
- Ongoing, high\-level training and professional development
- Collaboration with top\-tier coaches in the industry
- Quarterly bonus opportunities based on performance and impact
- Paid training
About Amplify AI Coaching
Amplify AI Coaching is one of the fastest\-growing personal and professional development companies focused on helping individuals integrate AI to enhance their lives and performance.
With a global audience of over 300 million people engaging with our content, many go on to invest in our highest level of support, one\-on\-one coaching.
Our coaching team delivers over 700 sessions weekly across more than 50 countries.
We are committed to excellence in every aspect of our work, and that starts with the caliber of people we bring onto our team.
Pay: $65\.00 \- $80\.00 per hour
Benefits:
- Flexible schedule
Work Location: Remote
Salary Context
This $135K-$166K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amplify AI, LLC, 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. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $135K to $166K.
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.
Amplify AI, LLC AI Hiring
Amplify AI, LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $166K - $166K.
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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