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About This Role
Role Overview
Engenious University is building the career infrastructure for the next generation of QA professionals.
Our mission is to help experienced QA professionals successfully navigate the transition toward AI. Through live cohort programs, mentorship, internships, and hands\-on training, we help engineers become AI Testing Engineers, AI Quality Engineers, and AI\-native automation specialists.
We are looking for an AI\-First Marketing Manager to own growth for one of our flagship programs, someone who can develop positioning, design acquisition systems, create content, launch campaigns, run webinars and events, optimize funnels, analyze performance, and continuously improve what works. You will own the entire marketing engine from awareness to enrollment. Your responsibility is not activity. It is outcomes: qualified leads, applications, enrollments, and revenue.
Because we operate as a lean, high\-performance team, AI is a core part of how we work. You'll use tools such as Claude, Gemini, ChatGPT, and automation platforms to execute at a high level. More importantly, you'll turn successful workflows into repeatable systems, prompt libraries, AI agents, and automations that make the entire company faster.
If you thrive on ownership and believe AI should fundamentally change how marketing teams operate, you'll feel at your place here!
What you will be doing
- Owning the positioning, messaging, and go\-to\-market strategy for one of our flagship programs
- Building and optimizing the full marketing funnel from awareness to enrollment
- Managing and scaling content across LinkedIn, YouTube, email, and community channels
- Planning, launching, and analyzing campaigns, events, and conversion experiments
- Optimizing landing pages and conversion flows to maximize lead generation, applications, and booked calls
- Leveraging AI tools and automations to increase marketing efficiency and output
- Working closely with Sales to improve lead quality, conversion rates, and revenue impact
- Establishing a scalable, AI\-first marketing engine that consistently generates qualified leads
- Owning landing page performance and continuously improving conversion rates from visitors to subscribers, qualified leads, applications, and booked calls
- Creating repeatable systems, workflows, and automations that improve team productivity
- Driving measurable growth through experimentation, analytics, and continuous optimization
Candidate requirements
- 4\+ years of experience in Growth, Product, Content, or Digital Marketing
- Experience generating qualified leads and owning marketing outcomes
- Experience with HubSpot or a comparable CRM and marketing automation platform. Familiarity with tools such as PostHog, Jira, Confluence, Discord, and OKR\-based planning methodologies is advantageous.
- Strong written English and copywriting skills
- Daily experience using AI tools such as Claude, ChatGPT, Gemini, Perplexity, NotebookLM, or similar
- Advanced practical experience with designing and implementing AI\-powered workflows, automations, prompt libraries, custom GPTs, Claude Skills, and other productivity\-enhancing AI systems
- Understanding of software development processes and technical audiences
- Experience marketing to technical, B2B, or education\-focused audiences, particularly within EdTech, online learning, or cohort\-based programs
Nice to have
- Experience marketing to QA Engineers, Software Engineers, Developers, or other B2B technical audiences is a strong plus
- Experience growing LinkedIn, YouTube, Instagram, Reels, Shorts, or other content\-driven channels
- Experience building and engaging communities through Discord, Slack, Reddit, or similar platforms
- Experience producing and promoting webinars, workshops, meetups, or educational events
- Experience building referral, affiliate, or community\-led growth programs
- Basic knowledge of Figma, Canva, Midjourney, Runway, or other AI\-powered creative tools
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 Engenious, 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.
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.
Engenious AI Hiring
Engenious has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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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