Interested in this AI/ML Engineer role at Parenthood Together?
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About This Role
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.
About the Role
This is a six\-week paid internship for someone who can design and build the AI agent stack that runs Parenthood Together's operations. You will automate communications, content workflows, and reporting using Claude and Zapier — and document everything so the system runs without you long after the internship ends.
The output is not a prototype or a proof of concept. It is a working system, delivered to the company. You will work directly with the founder and present the completed agent stack at the end of the program.
What You Will Own
Discovery \& Architecture (Week 1\)
– Audit existing operations across communications, content, and reporting to identify the highest\-value automation opportunities
– Map current workflows and define which tasks are ready to automate versus which need to be stabilized first
– Align with the founder on priorities and deliver an automation roadmap
Build \& Configure (Weeks 2–5\)
– Build Claude\-based workflows for content drafting, repurposing, and asset generation
– Configure Zapier connections across tools and platforms — publishing, email, scheduling, reporting
– Automate reporting with AI\-generated performance summaries and digests
– Test every workflow in production conditions, not just in a sandbox
Documentation \& Handoff (Week 6\)
– Document every system you build — clearly enough that a non\-technical founder can operate it independently
– Present the completed AI agent stack to the founder: what it does, how it runs, and how to maintain it
– Identify what to build next so the automation roadmap continues after you leave
What You Will Learn
– How to design AI automation systems for a real content business, not a classroom environment
– How to translate operational bottlenecks into automation architecture that actually holds
– How to build and document systems that outlast your direct involvement
– How a content brand's technology decisions compound over time — and how to make the right ones early
Who We Are Looking For
– Hands\-on experience with the Claude API — you have built something with it, not just used it in a chat interface
– Working knowledge of Zapier or a comparable automation platform (Make, n8n)
– Able to build in production and ship working systems, not just prototypes
– Strong documentation instincts — a system no one can operate is not a finished system
– Curious about how content brands operate and willing to learn the business context, not just the tools
– Python or JavaScript experience is a plus
– Recent graduate, current student, or self\-taught builder welcome
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, content operations, AI\-first workflows, 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 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 Role Is Different
Most technical internships ask you to contribute to a system someone else designed. This one asks you to design it. You will audit a real business, decide what matters most to automate, and build the infrastructure that lets a small team operate at a scale most startups cannot reach this early. The system you deliver will run the company's communications, content operations, and reporting after you leave. That is a different kind of responsibility than most interns ever carry.
To Apply
Send a short note (no formal cover letter) addressing the following:
– One social campaign, content system, or technical project 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 site, a portfolio, a GitHub repo
Email: [email protected]
Application deadline: June 15, 2026
Pay: $10\.00 \- $15\.00 per hour
Work Location: Remote
Salary Context
This $20K-$31K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Parenthood Together, 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 $178,940 based on 11,900 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380. This role's midpoint ($26K) sits 85% below the category median. Disclosed range: $20K to $31K.
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/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 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).
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 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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