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About This Role
Social Media and AI Automation Intern
*Parenthood Together* *\| Paid \| Remote \| July 6 to August 14, 2026*
About Parenthood Together
Parenthood Together is a content brand for parents who want better information and a smarter community around everyday parenting. We help parents find products that earn trust, routines that hold up under daily pressure, and the perspective that comes from other parents doing the work alongside them. Our editorial content, Facebook community, and Substack carry the conversation. Our first parent\-voted product awards launch publicly on November 4, 2026\.
About the Role
This is a six\-week paid internship designed to give one intern direct ownership of social strategy and execution across five platforms, plus a capstone project that builds AI agents to automate ongoing content operations.
You will work directly with the founder. You will leave with a published body of work, a working AI agent system you designed, and the operating fluency that comes from joining a company at its founding stage.
What You Will Own
Phase 1: Strategy (Weeks 1 to 2\)
- Partner with the founder to shape social strategy across Meta, YouTube, Pinterest, TikTok, and X
- Map content pillars to the awards launch, Substack, blog, and Facebook community
- Audit existing content and identify the highest leverage growth opportunities
- Draft a six\-week content calendar aligned to platform\-specific best practices
Phase 2: Production (Weeks 3 to 4\)
- Lead post creation and ideation across all five platforms
- Batch creative assets in Canva using established brand templates
- Adapt long\-form content from the site and Substack into platform\-native formats
- Coordinate publishing cadence and audience engagement across channels
Phase 3: Automation and Capstone (Weeks 5 to 6\)
- Build the AI agent stack that will run social operations after the internship ends
- Configure Claude\-based workflows for drafting, repurposing, and asset generation
- Set up Blotato for cross\-platform publishing and distribution
- Document the system so the founder and future team members can operate it
- Present the capstone to the founder: a working AI agent system delivered to the company
What You Will Learn
- How to build a content brand from zero alongside an experienced operator
- How to design AI agents that compress weeks of marketing work into hours
- How to align brand, audience, and revenue as one integrated system
- How modern startups use content as the front door to community and partnerships
Who We Are Looking For
- Hunger to learn and a strong instinct for problem solving
- Recent graduate, current student, or self\-taught builder with a clear interest in community growth and AI automation
- Working knowledge of Canva and comfort with rapid prototyping
- Curious about how AI agents operate end to end and willing to go deep on Claude, Blotato, and adjacent tools
- Strong writing instincts and a feel for what reads as human versus generated
- Background or interest in the family and parenting space is a plus, not a requirement
The Mentorship
The founder has 25 years of experience building global media and advertising campaigns at scale and has led multiple companies through founding stages. Weekly working sessions will cover brand strategy, partnership thinking, AI\-first operations, and the principles behind building a category brand. Few internships put you across the table from this depth of operating experience.
Logistics
- Dates: July 6 to August 14, 2026 (six weeks)
- Format: Remote, with scheduled weekly working sessions
- Time commitment: 8 hours per week
- Compensation: $1,000 program stipend
- Academic credit: Eligible where applicable. We will coordinate with your school's program coordinator on request.
- Outcome: Strong performers will be considered for a continued role with the company
Program Terms
- This is a fixed\-term internship. Continuation beyond the program is not guaranteed and would require a separate offer.
- Engaged on a 1099 contractor basis. Standard onboarding paperwork includes a W\-9 and a confidentiality agreement.
- Applicants must be authorized to work in the United States for the duration of the program.
- Parenthood Together is an equal opportunity employer. We welcome applicants of all backgrounds.
Why This Internship Is Different
Most internships place you inside a system that is already running. This one places you inside one that is being built. You will see what it takes to earn the trust of parents who have been talked at by every brand in the category. You will help shape a community that treats its members as the experts they already are. You will build the AI systems that let a small team stay close to the people they serve. The work you publish will sit on our channels. The system you build will run our operations.
To Apply
Send a short note (no formal cover letter) addressing the following:
- One social campaign you wish you had built, and why
- One AI tool or workflow you think more brands should be using
- A link to anything you have made: a post, a project, a Substack, a feed, a portfolio
Email applications to: [email protected]
Application deadline: June 15, 2026
Send a short note (no formal cover letter) addressing the following:
- One social campaign you wish you had built, and why
- One AI tool or workflow you think more brands should be using
- A link to anything you have made: a post, a project, a Substack, a feed, a portfolio
Email applications to: [email protected]
Application deadline: June 15, 2026
Pay: $15\.00 \- $18\.00 per hour
Work Location: Remote
Salary Context
This $31K-$37K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $212K across 45 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Parenthood Together, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($34K) sits 86% below the category median. Disclosed range: $31K to $37K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Parenthood Together AI Hiring
Parenthood Together has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Remote, US. Compensation range: $31K - $37K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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