Interested in this AI/ML Engineer role at Meta?
Apply Now →About This Role
We're looking for a 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 role requires proven developer marketing and technical marketing experience; this is not a generalist brand or campaign role. This person will own end-to-end partner marketing programs from strategic planning through to measurement, with a particular focus on ISV partners who are building AI-powered products, integrating foundation models, and deploying AI solutions on our platforms. They will serve as the marketing voice in cross-functional partner relationships, deeply understanding how ISVs evaluate, adopt, and go to market with AI platforms.They will operate in a fast-moving, rapidly changing environment where priorities shift quickly, making decisions with incomplete information and reprioritizing based on business impact. They will operate as an AI-native marketer, leveraging AI tools and workflows to move faster, generate deeper insights, and scale impact beyond what traditional approaches allow. Above all, they bring a genuine passion for AI, an understanding of the ISV business model, and an audience-first mindset and a deep understanding of technical audiences and translating complex AI capabilities into compelling 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, driving mutual business outcomes including awareness, pipeline, adoption, and revenue
- ISV Ecosystem Development: Build deep, trust-based relationships with ISV partner marketing teams
- understand their product roadmaps, AI adoption maturity, and go-to-market strategies to identify the highest-impact co-marketing opportunities
- Cross-Functional Orchestration: Lead cross-functional workstreams spanning product marketing, sales, developer relations, comms, events, brand and creative, aligning internal teams and external ISV partner counterparts on shared goals
- Campaign & Content Development: Design and deliver multi-channel campaigns (digital, events, thought leadership, case studies, technical webinars, developer workshops) that showcase joint AI solutions, highlight ISV integration stories, and drive measurable demand among technical and business audiences
- Measurement & Optimization: Define KPIs, build dashboards, and run post-campaign analyses to quantify partner marketing ROI
- use data to continuously improve program performance
- AI & 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
- AI-Native Execution: Integrate AI tools into daily workflows
- from research and content generation to campaign analysis and reporting and champion AI adoption within the broader marketing team
- Budget & Resource Management: Own the partner marketing budget, negotiate co-funding / MDF agreements with ISV partners, and manage agency/vendor relationships
- Executive Storytelling: Craft compelling 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
- Technical Audience Marketing: Develop and execute marketing programs specifically designed for technical audiences within ISVs developers, AI/ML engineers, solution architects, and platform builders. Understand their workflows, pain points, and decision-making processes around AI adoption. Create content that earns credibility with technical buyers (architecture guides, AI integration playbooks, model deployment tutorials, technical webinars) rather than generic brand messaging
- Navigating Ambiguity: Operate effectively in a fast-paced, constantly evolving AI landscape, making decisions with incomplete information, reprioritizing quickly, and maintaining momentum through organizational and market shifts
Minimum Qualifications:
- 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
- Strong analytical skills, comfortable building business cases, defining measurement frameworks, and making data-driven decisions
- Exceptional written and verbal communication
- ability to influence senior executives internally and at ISV partner organizations
- Experience working in matrixed, global organizations with the ability to drive alignment across diverse stakeholders
- Demonstrated ability to thrive in fast-paced, high-growth environments with shifting priorities and ambiguity
- Deep passion for AI and genuine curiosity about foundation models, generative AI, AI agents, and the rapidly evolving AI ecosystem
Preferred Qualifications:
- Experience in B2B technology marketing at AI-focused companies or major platform companies or at an ISV that built on these platforms
- Experience in a high-growth or hyper-scale technology company where speed and adaptability were essential
- Deep background in AI/ML, foundation models, generative AI, or enterprise AI platform ecosystem
- 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 with ISV partner programs at scale (e.g., technology alliance programs, ISV accelerators, marketplace listings). MBA or an advanced degree in marketing, business, or a related field
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Familiarity with the AI developer tooling landscape (model APIs, fine-tuning platforms, vector databases, AI orchestration frameworks)
- Prior experience at or marketing to ISVs, particularly those integrating AI capabilities into their products or building AI-native solutions
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 [email protected].
$124,000/year to $178,000/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 $124K-$178K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($151K) sits 29% below the category median. Disclosed range: $124K to $178K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Meta AI Hiring
Meta has 50 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety, AI Software Engineer, Research Engineer. Positions span New York, NY, US, Menlo Park, CA, US, Santa Clara, CA, US. Compensation range: $152K - $361K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.