Interested in this AI/ML Engineer role at Blavity Inc.?
Apply Now →About This Role
Blavity, Inc. is a venture\-funded media and technology company, founded in 2014 around a simple idea: enable Black millennials to tell their own stories. Today, we are home to the largest network of platforms and lifestyle brands serving the multifaceted lives of Black millennials \& Gen Z through original content, video, unique experiences, and product solutions. Blavity Inc. has evolved into a market leader for Black media, reaching 250 million users per month through our growing brand portfolio, including Blavity, AfroTech, Talent Infusion, Travel Noire, HealthStack, and Blavity House Party. Our Blavity, Inc. employee community is composed of passionate, energetic, and culturally conscious individuals working together to deliver value to each other, the company, and our clients. We are always searching for new additions to our community that will help us continue to scale, contribute meaningfully to our culture, and advance our strategic direction.
Job Summary:
Blavity Inc. is looking for a Content Marketing Manager, AI to own the content engine and value\-based storytelling that positions AI Edge as the authority of the tech ecosystem. This role is responsible for building evergreen \& timely AfroTech AI education and AI Edge content across its social channels, the AI Edge newsletter, creator content. This role ensures everything we publish is educational, value\-based, and positions our brand as THE experts in tech, AI, and career growth.
The ideal candidate understands and is passionate about the tech ecosystem and AI topics, treats content as a brand\-authority engine, and is comfortable producing the work themselves, including being on camera to test concepts. Success will be AI Edge newsletter sign\-ups and social engagement.
This role will report directly to the Chief of Staff with a dotted\-line relationship to the VP of Digital Operations.
Responsibilities:
- Own the AI Edge content engine: long\-form blog posts, thought leadership articles, and evergreen video content on tech trends, AI topics, and upskilling for founders, corporate professionals, and techies.
- Curate and write a bi\-weekly AI Edge newsletter sends
- Transform monthly AI Edge workshop recordings into multi\-channel content marketing material
- Coordinate with tech and AI creators to identify timely content opportunities and approve suggested video pitches.
- Work with Black Tech Green Money to ensure podcast clips are strong, valuable, and ensure episodes and clips make it over to our YouTube channel
- Partner with our SEO team to create blog\-post like educational content
- Ensure all evergreen content is value\-based, educational, and positions the brand as the expert of the tech ecosystem
- Develop paid marketing content and partner on the paid ad strategy
- Nice to have: Be comfortable on camera to test concepts and formats
Qualifications:
Education: Bachelor's degree or equivalent professional experience
Required Experience:* 4\+ years in content marketing, content production, or a comparable role, ideally in a media, community, creator\-led, or tech brand environment
- Demonstrated passion for and fluency in the tech ecosystem, technology topics, and AI trends
- Strong long\-form writing and storytelling skills that make complex tech and AI topics accessible and valuable
- Experience directing creators or contributors and maintaining content quality
- Comfortable being on camera to test and present concepts
Preferred Experience:* Content experience in the tech, AI, startup, or career\-development space
- Experience with newsletters, webinars, and YouTube or podcast content workflows
- Experience developing paid marketing content or partnering on paid ad strategy
Technologies:
Fluent in Google Suite, Asana, and/or comparable project management suite; working experience with newsletter and YouTube content tools
Additional Qualifications:* Independent, entrepreneurial self\-starter comfortable with a high level of responsibility
- Experimental, forward\-thinking mindset with a strong interest in testing new content ideas and formats
- Strong comfort with ambiguity and a bias toward action
- High\-energy, company\-first, positive attitude; willing to wear multiple hats
- Excellent communication skills; outgoing and sociable
- A healthy appreciation of GIFs and Black culture
Details:* This is a fully remote, U.S.\- based role. Occasional travel may be required.
- Candidates must be authorized to work in the U.S.
- Candidates must be available to work in alignment with the Pacific Time Zone.
- The annual salary range for this role is $30 \- $40/hr
- This position will be employed through Blavity's Employer of Record (EOR) partner. The selected candidate will be an employee of the EOR and assigned to provide services to Blavity.
*To apply, please submit your resume and cover letter online at* *BlavityInc.com/Careers**.*
*Blavity is committed to creating a diverse environment free of discrimination and harassment, and building a team that represents a variety of backgrounds, perspectives, and skills.*
*Blavity is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, HIV Status, veteran status, or any other status protected by the laws or regulations in the locations where we operate.*
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Salary Context
This $62K-$83K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Blavity Inc., 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($72K) sits 67% below the category median. Disclosed range: $62K to $83K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Blavity Inc. AI Hiring
Blavity Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $83K - $83K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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