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Head of Fundraising
Engage Media Inc. d/b/a Engage60 — Founding Leadership Position Full-Time (preferred) or Part-Time Compensation: Meaningful Equity + Defined Deferred Salary + Cash on First Close Current Raise: $1M Pre-Seed at a $12M Post-Money SAFE
Why This Role Exists
Engage60 is raising a $1M pre-seed round to launch nationally in the next 60 days. We have a finished product and no fundraising department. We are hiring the person who builds both.
This is a founding leadership seat, not an execution role. You will own the round, own the investor network, and own the capital strategy that carries Engage60 through Series A and beyond. If we raise this round, you will have been the reason.
About Engage60
Engage60 is building the first short-form civic news app — a mobile-first product that helps people discover, understand, and engage with what is actually happening in American news and politics. Think of the format you already use every day, applied to the subject that shapes everything else.
Where we are today:
- Fully built and shipping product — not a deck, not a prototype
- National launch scheduled within 60 days
- Founding team in place, with product development accelerating
- Creator-led growth engine in early build
Where we're going: Short-form civic news → national consumer audience → civic engagement platform → civic intelligence platform → B2B media and political intelligence.
That sequence is the reason this round exists. The person who builds our first investor base is the person best positioned to lead every round after it.
The Opportunity
You will work directly with the Founder & CEO on the company's fundraising strategy and investor relationships, with significant autonomy over sourcing, outreach, pipeline, positioning, and process.
Your primary mission:
Raise the $1M pre-seed round and build the investor network that carries Engage60 through its next stages of growth.
This role suits someone who already has real relationships across venture capital, angel investing, family offices, strategic investors, founders, and high-net-worth individuals — and who has been waiting for the right company to bring them to.
What You'll OwnThe Raise
- Lead the $1M pre-seed round end to end
- Develop and execute the investor strategy and target list
- Build and maintain a disciplined investor pipeline with accurate records
- Source qualified investors through your network and targeted outreach
- Generate warm introductions to VC firms, angels, family offices, strategic investors, and media companies
- Coordinate investor meetings and drive follow-ups
- Move investors from introduction → diligence → commitment
- Sit alongside the Founder & CEO in meetings and negotiations
Investor Relations
- Build durable relationships with current and prospective investors
- Communicate Engage60's vision, product, model, and growth path with conviction
- Cultivate investors who return for future rounds
- Build our bench of strategic advisors and investors
- Target investors who bring more than capital — distribution, media relationships, technical depth, strategic partnerships
Capital Strategy
- Advise the founding team on fundraising strategy and investor positioning
- Sequence who we approach, and when
- Sharpen fundraising materials and investor communications
- Feed investor conversations back into the pitch
- Identify strategic and non-dilutive financing opportunities
- Help architect the long-term capital plan through Series A and Series B
Compensation
We've designed this to align incentives honestly and to pay you for the work you actually do. No vague promises.
- Equity
A meaningful equity grant in Engage60, recognizing that you join at the founding stage and build the company's capital base. Documented by formal equity agreement, subject to Company approvals and standard vesting terms.
- Deferred Salary
An agreed monthly amount, accruing from your start date, documented in writing before you begin.
- Cash on First Close
The first $250,000 closed into the round triggers immediate payment of accrued deferred salary to date. Additional closes accelerate the remainder. Deferred salary is fully payable on completion of the round.
The milestone, defined: "Successful completion" means $1,000,000 in aggregate closed subscription proceeds into the pre-seed round. Partial acceleration at $250K. No ambiguity, no renegotiation.
Why This Role Is Different
You're not inheriting a function. You're building it.
You will work directly with the Founder & CEO, hold real ownership over the outcome, and have a clear path to becoming one of Engage60's core leaders — with the opportunity to stay deeply involved as the company moves through each stage of the ladder above.
The person who raises this round doesn't hand off the relationship. They own it into the next one.
First 90 Days
Days 1–30 — Build the Engine
- Prioritized investor target list, tiered by fit
- Activate your existing investor relationships
- Outreach systems and pipeline tracking live
- First investor meetings underway
- Messaging refined against real investor feedback
Days 31–60 — Build Momentum
- Pipeline expanded and qualified
- Meeting volume up
- Strategic investor and media company relationships opened
- Investors advancing into diligence
- First meaningful commitments secured
Days 61–90 — Close the Round
- Driving the pre-seed toward completion
- Strong investor syndicate assembled
- Future-round relationships established
- Foundation for the Series A strategy in place
Who We're Looking For
Highly connected. Entrepreneurial. Persuasive. Relentless. Comfortable building from zero.
Background may include: venture capital, investment banking, private equity, family offices, startup fundraising, investor relations, corporate development, strategic finance, entrepreneurship, startup advisory, or capital formation.
What matters most:
- A demonstrated history of raising or directly contributing to successful financings
- A meaningful network across VC, angels, family offices, founders, or strategic investors
- Strong communication and relationship-building ability
- Confidence pitching an early-stage technology company
- Working command of the venture fundraising process
- Excellent organizational and follow-up discipline
- Ability to work directly with founders and executives
- An operator's mindset — independent, fast, low-ego
An established investor network is strongly preferred. If you have it, we should talk this week.
This Role Is For You If…
You read:
Finished product + $1M Pre-Seed + National launch in 60 days
…and immediately start thinking about who needs to be in the room.
You have the relationships. You know how fundraising works. You know how to get investors on the phone, how to create momentum, and how to close. You want to build something from the ground up and put your name on it.
If that's you, we want to hear from you.
How to Apply
Submit:
- Resume or LinkedIn profile
- Brief overview of your fundraising experience
- Examples of financings you have personally sourced or helped close
- Types of investors in your existing network
- Typical check sizes of your investor relationships
- Brief explanation of why you're a strong fit for Engage60
Candidates with a demonstrated track record of sourcing and closing startup investment are strongly encouraged to apply.
Engage Media Inc. d/b/a Engage60 is an equal opportunity company.
Benefits:
- Flexible schedule
- Paid time off
Work Location: Remote
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Engage60, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Engage60 AI Hiring
Engage60 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 $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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