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About Educate 360
Educate 360 is a family of specialized brands with a joint mission: to help individuals and organizations gain the skills needed to thrive in today’s technology\-led and innovative\-driven economy. Educate 360 develops integrated solutions across brands to achieve enterprise customers’ learning \& development goals in Management \& Leadership, Data Science, and IT skills. With locations across the U.S. and Europe, we have brought our instructor\-led and on\-demand training, coaching, and consulting to numerous individuals and organizations.
Position Summary
The AI Adoption Business Development Executive is responsible for driving net\-new revenue growth and expanding Educate360's market presence for the AI Adoption Program. This is a pure hunting role focused on identifying, engaging, and acquiring new customers within a designated territory of Open for Prospecting accounts in NA. This individual owns the front\-end sales cycle, builds pipeline, creates demand, leads customer conversations, and closes initial AI Adoption Program opportunities before transitioning customers to the appropriate Account Executive for long\-term account management and expansion.
Revenue Growth \& Business Development
- Own a revenue quota and commission plan tied directly to AI Adoption Program sales.
- Develop and execute a territory plan focused on Open for Prospecting accounts throughout the United States.
- Generate net\-new opportunities through outbound prospecting, cold calling, email campaigns, LinkedIn outreach, referrals, events, and networking.
- Build and manage a healthy pipeline of qualified opportunities.
- Lead discovery conversations with business leaders and executive stakeholders.
- Position Educate360 AI Adoption solutions and close initial engagements.
- Maintain accurate CRM records, forecasting, and pipeline management.
AI Adoption Market Development
- Develop deep expertise in the AI Adoption Framework, including Align, Learn, and Apply.
- Help establish Educate360 as a recognized leader in workforce AI enablement.
- Stay informed on Microsoft Copilot, workplace AI trends, and adoption challenges.
- Provide customer and market feedback to support offer evolution and growth.
- Identify new industries, buyer personas, and growth opportunities.
Cross\-Functional Collaboration
- Partner with Solution Specialists to accelerate product knowledge and support advanced customer conversations when needed.
- Collaborate with Marketing and Sales Leadership on campaigns and market awareness initiatives.
- Ensure smooth handoff of closed business to the appropriate Account Executive.
Success Measures
- Revenue attainment against quota.
- Net\-new customer acquisition.
- Qualified pipeline creation.
- AI Adoption Program opportunities closed\-won.
- Territory penetration and prospect engagement.
- Forecast accuracy and CRM discipline.
- Contribution to AI Adoption Program market awareness and growth.
How Success Will Be Measured
- Consistently achieves or exceeds quarterly and annual revenue targets.
- Builds a sustainable pipeline with appropriate coverage against quota.
- Creates measurable growth within assigned Open for Prospecting accounts.
- Demonstrates strong activity metrics including prospecting, meetings, and opportunities created.
- Successfully converts net\-new prospects into paying AI Adoption Program customers.
- Executes disciplined CRM management and forecasting practices.
- Contributes to expanding Educate360 brand awareness and market traction in AI adoption.
Why Educate 360?
We believe that great ideas emerge when people collaborate in an environment where thoughts and perspectives can be freely shared. Effective teamwork happens when every member feels empowered, valued, and respected. We are committed to fostering a culture where everyone is accepted, included, and encouraged to contribute in meaningful ways.
Benefits: We're committed to supporting our employees’ health, financial stability, and overall well\-being. Our comprehensive benefits include competitive Paid Time Off (PTO), Medical, Dental, and Vision plans, 100% company\-paid Life and Disability insurance, and a generous 401(k) matching program.
Equal Opportunity Employer: Educate 360 is committed to providing equal employment opportunity for all persons regardless of race, color, religion, sex, age, marital status, national origin, citizenship status, disability, or veteran status.
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 Educate 360, 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 $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.
Educate 360 AI Hiring
Educate 360 has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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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