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We're looking for a seasoned Partner Marketing Manager with deep roots in the AI and ISV (Independent Software Vendor) ecosystem to drive co\-marketing strategy and execution across our AI partner landscape. This person will own end\-to\-end partner marketing programs, from strategic planning through measurement, with a focus on ISV partners building AI\-powered products, integrating foundation models, and deploying AI solutions on our platforms.They will prioritize competing workstreams based on impact and stakeholder needs in an environment where priorities and market conditions evolve quickly, leveraging AI tools and workflows to generate insights and scale impact. They will serve as the marketing lead in cross\-functional partner relationships, understanding the ISV business model and translating complex AI capabilities into stories that resonate with ISV developers, product leaders, and executives.
### Partner Marketing Manager, AI Responsibilities:
- Partner Marketing Strategy: Develop and execute integrated co\-marketing plans with strategic ISV partners who are building on, integrating with, or deploying AI solutions using our platforms and APIs. Drive mutual business outcomes including awareness, pipeline, adoption, and revenue
- ISV Ecosystem Development: Build relationships with ISV partner marketing teams. Understand their product roadmaps, AI adoption maturity, and go\-to\-market strategies to identify high\-impact co\-marketing opportunities
- Cross\-Functional Orchestration: Lead cross\-functional workstreams spanning product marketing, sales, developer relations, comms, events, brand, and creative. Align internal teams and external ISV partner counterparts on shared goals
- Campaign, Content, and Executive Storytelling: Design and deliver multi\-channel campaigns (digital, events, thought leadership, case studies, technical webinars, developer workshops) that showcase joint AI solutions and drive measurable demand. Craft joint AI value propositions and narratives for C\-suite and VP\-level ISV partner stakeholders. Present at partner summits, AI industry events, and internal leadership forums
- Measurement and Optimization: Define KPIs, build dashboards, and run post\-campaign analyses to quantify partner marketing ROI. Use data to continuously improve program performance
- AI and ISV Ecosystem Intelligence: Monitor the competitive AI landscape, ISV partner dynamics, foundation model ecosystem, and industry trends to identify whitespace opportunities and inform go\-to\-market strategy. Integrate AI tools into daily workflows, from research and content generation to campaign analysis and reporting
- Budget and Resource Management: Own partner marketing budget, negotiate co\-funding/MDF agreements with ISV partners, and manage agency/vendor relationships
- Technical Audience Marketing: Develop and execute marketing programs for technical audiences within ISVs (developers, AI/ML engineers, solution architects, platform builders). Understand their workflows, pain points, and decision\-making processes for AI adoption. Create content that earns credibility with technical buyers (architecture guides, AI integration playbooks, model deployment tutorials, technical webinars)
### Minimum Qualifications:
- 10\+ years of experience in technology marketing, with 6\+ years in partner, channel, or alliance marketing at a technology company
- Bachelor's degree in Business, Marketing, Creative discipline, or equivalent practical experience
- Experience building and scaling co\-marketing programs with ISV partners, technology platforms, or enterprise SaaS companies in the AI, cloud, or developer tools space
- Direct experience working with or at ISVs, understanding their business models (SaaS licensing, platform dependencies, channel dynamics) and how they evaluate and adopt AI platforms
- Experience managing marketing budgets and co\-investment frameworks (MDF, joint funding) with ISV and technology partners
- Analytical experience including building business cases, defining measurement frameworks, and making data\-driven decisions
- Communication experience with ability to influence executives internally and at ISV partner organizations
- Experience working in matrixed, global organizations with the ability to drive alignment across diverse stakeholders
- Experience in fast\-paced, high\-growth environments with shifting priorities
- Technical acumen sufficient to understand AI/ML concepts, APIs, SDKs, model architectures, and platform integrations, and to translate technical AI capabilities into marketing narratives
- Experience marketing to technical audiences (developers, AI/ML engineers, solution architects, IT leaders) with a portfolio demonstrating understanding of how technical buyers evaluate and adopt AI technology
### Preferred Qualifications:
- Experience at or marketing to ISVs integrating AI capabilities into their products or building AI\-native solutions, including ISV partner programs at scale (e.g., technology alliance programs, ISV accelerators, marketplace listings)
- Background in AI/ML, foundation models, generative AI, or enterprise AI platform ecosystems, including familiarity with AI developer tooling (model APIs, fine\-tuning platforms, vector databases, AI orchestration frameworks)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience driving organizational change and innovation in marketing
- MBA or advanced degree in marketing, business, or related field
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience in B2B technology marketing at AI\-focused companies, major platform companies, or at an ISV that built on these platforms
- Experience with developer relations, developer marketing, or technical community programs (DevRel, hackathons, AI developer conferences, open\-source community engagement)
### About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E\-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations\[email protected].
$152,000/year to $213,000/year \+ bonus \+ equity \+ benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
Salary Context
This $152K-$213K range is above the median 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 Meta, 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 ($182K) sits 17% below the category median. Disclosed range: $152K to $213K.
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.
Meta AI Hiring
Meta has 26 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer, AI Product Manager, LLM Engineer. Positions span Menlo Park, CA, US, Burlingame, CA, US, New York, NY, US. Compensation range: $181K - $356K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
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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