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About This Role
About Neo4j:
Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise\-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy\-to\-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter.
Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business.
Our Vision:
At Neo4j, we have always strived to help the world make sense of data.
As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive.
Job Overview
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As Product Marketing Manager, AI Capabilities \& Developer Enablement, you own how Neo4j’s AI capabilities show up to the developer audience, how our technology integrations are positioned, and the sales enablement infrastructure that turns capability stories into field behavior. You translate GraphRAG, agents, agent memory, MCP, and our integration ecosystem into messaging that earns developer trust, then build the plays, decks, and certification that help the field win deals with it.
This is a builder’s role on a small, senior, systems\-first team. You own the messaging architecture, the proof, and the field\-facing assets for the developer persona, and you pull on teammates, agentic pipelines, and partners to deliver.
The ideal candidate is part developer marketer, part field operator, and part builder. You are comfortable getting hands\-on with the product (a free AuraDB instance, a GraphRAG demo, an integration with an agent framework), and equally comfortable turning what you learn into a one\-page sales play that a seller can run the next morning.
Key Responsibilities
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- AI capabilities messaging for developers: Contributed to the messaging architecture and proof points for Neo4j’s AI capabilities, targeting technical, developer, and AI\-engineering audiences. Translate GraphRAG, agents, agent memory, and Cypher AI into credible, differentiated stories that respect developer intelligence.
- Technology integrations: Own positioning and messaging for Neo4j’s integration ecosystem, including agent frameworks (LangChain, LlamaIndex), MCP servers, cloud and data\-platform partners (Databricks, Snowflake, Microsoft), and the connectors and APIs developers build with. Make the ecosystem story part of the core narrative, not an appendix.
- Sales enablement infrastructure: Build and maintain the enablement that turns capability stories into field behavior: first\-call decks, talk tracks, objection\-handling guides, discovery question banks, demo guidance, and live training. Design it as a system the field actually uses, not a pile of one\-off decks.
- Win\-loss analysis: Partner with sales, sales engineering, and revenue operations to run structured win\-loss programs. Surface patterns by competitor, segment, and use case, and feed insights back into messaging and product priorities.
- Sales enablement: Build and deliver enablement that arms global sellers and SEs to handle competitive deals with confidence. This includes objection\-handling guides, talk tracks, discovery question banks, demo guidance, and live training sessions.
- Launch support: Partner on AI capability and integration launches, owning developer\-facing messaging, demos, and enablement so each release lands with the field and the community.
- Customer zero: Use the product the way our developers do. Prototype with it, build demos, and bring that hands\-on credibility into every story you tell.
Requirements/qualifications
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- Bachelor’s degree in Marketing, Business, Engineering, or a related field; MBA a plus. 3\+ years in product marketing, developer marketing, sales enablement, or go\-to\-market strategy within the technology/software industry.
- Direct experience marketing to a developer or technical\-practitioner audience, ideally for data infrastructure, databases, developer tools, or AI/ML platforms.
- Proven track record building sales enablement that the field actually uses: first\-call decks, plays, objection handling, and certification, plus experience running or contributing to a structured win/loss program.
- Technical acumen to get hands\-on with the product, read documentation and architecture diagrams, and form a defensible point of view. You do not need to be an engineer today, but you should be able to learn complex technical categories quickly.
- Strong writing and presentation skills. You can write a one\-page play and design a 30\-minute certification session with equal ease.
- Proven ability to work across product, sales, sales engineering, DevRel, and partner marketing, and to move work forward through influence rather than authority.
- Familiarity with GraphRAG, agents, vector databases, knowledge graphs, or GenAI architectures is a strong plus. Comfort using AI tools in your own workflow is expected; we are an agent\-native team.
The annual base salary range for this position based in the United States is listed below. This salary range is an estimate, and the actual salary may vary based on Neo4j’s compensation practices, job related skills, depth of experience, relevant certifications and trainings, in addition to geographic location. Based on the factors above, Neo4j utilizes the full width of the range.
In addition to the range below, US employees are eligible for a stock option grant and certain roles are eligible for an annual bonus. Employees in this position are also eligible to participate in the Company’s standard benefit programs, which currently include the following: medical, dental, and vision benefits, 401(k), paid time off, and certain leaves of absence.
Annual Base Salary Range for This Role
$110,000 \- $160,000 USD
Why Join Neo4j?
Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j.
- Neo4j is one of the fastest\-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years.
- Raised the biggest funding round in database history ($325M Series F). Backed by world\-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well\-funded database companies in history.
- 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific, BT Group, Caterpillar, Cisco, Comcast, Department for Education UK, eBay, NBC News, Novo Nordisk, Worldline, and others.
- Co\-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success.
- Countless industry awards. Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform.
- A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers.
Research shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications. If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues.
One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career\-defining experience. We look forward to receiving your application.
Neo4j Values:
Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values:
(we)\-\[:VALUE]\-\>(relationships)
(we)\-\[:FOCUS\_ON]\-\>(userSuccess)
(we)\-\[:THRIVE\_IN]\-\>(:Culture {type: \[‘Open’, ‘Inclusive’]})
(we)\-\[:ASSUME]\-\>(:Intent {direction:’Positive’})
(we)\-\[:WELCOME]\-\>(:Discussions {nature: ‘IntellectuallyHonest’})
(we)\-\[:DELIVER\_ON]\-\>(ourCommitments)
Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide.
More information at www.neo4j.com.
Salary Context
This $110K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Neo4j, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($135K) sits 27% below the category median. Disclosed range: $110K to $160K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Neo4j AI Hiring
Neo4j has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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