Interested in this AI Software Engineer role at Meta?
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Meta is seeking a distinguished Software Engineer with deep AI specialization to drive transformative technical initiatives across Meta's AI\-powered products and platforms. In this role, you will define and lead the architectural direction of large\-scale AI systems — spanning foundation models, intelligent ranking and recommendation infrastructure, and applied machine learning pipelines — that serve billions of users across Meta's family of apps. You will identify and solve the hardest AI engineering challenges in the organization, set technical standards that span multiple teams, and leverage AI as a force multiplier to unlock capabilities previously considered intractable. This is a role for a technical leader who operates at the intersection of cutting\-edge AI research and production\-scale engineering, shaping both the systems and the culture that power Meta's AI future.
### Software Engineer, AI Specialist \- Monetization (Technical Leadership) Responsibilities:
- Identify and solve the most complex AI systems engineering challenges across the organization, including architecting large\-scale machine learning training and inference infrastructure that operates at Meta's global scale
- Define extensible technical foundations and cross\-organizational standards for AI model development, evaluation, and deployment pipelines that favor consistency and long\-term maintainability
- Drive the technical vision and multi\-year roadmap for AI platform capabilities, influencing priorities across multiple engineering teams and cross\-functional partners including research, product, and data science
- Evaluate emerging AI architectures, model paradigms, and industry developments to identify opportunities and risks relevant to Meta's competitive position, and translate findings into actionable engineering strategy
- Lead the design and implementation of AI systems where correctness, reliability, and performance are rigorously proven, establishing invariants and testing frameworks that prevent entire categories of model and system failures
- Identify where AI tooling and automation can eliminate entire categories of engineering work, and drive adoption of AI\-native workflows across engineering teams to exponentially increase organizational throughput
- Collaborate with research scientists and applied researchers to translate novel AI techniques from prototype into production systems that deliver measurable improvements to key product metrics
- Mentor engineers across the organization by providing customized technical coaching, leading engineering programs such as architecture reviews and AI craft initiatives, and establishing a culture of rigor and thoroughness
- Partner with legal, policy, and compliance teams to ensure AI systems meet privacy, security, and integrity standards, and set the bar for responsible AI development practices across the business domain
- Define new metrics and data\-driven decision\-making principles for long\-term, cross\-team AI initiatives, connecting technical outcomes to organization\-level priorities and business impact
### Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 12\+ years of experience in software engineering with a focus on AI, machine learning systems, or applied deep learning in production environments
- Experience architecting and delivering large\-scale AI or machine learning systems — including training infrastructure, model serving, ranking, recommendation, or foundation model pipelines — that operate at significant scale
- Experience leading multi\-team technical initiatives end\-to\-end, including defining strategy, driving cross\-functional alignment, and delivering measurable outcomes against organization\-level goals
- Experience identifying and resolving systemic engineering issues that span multiple systems or abstraction layers, including developing frameworks that prevent recurring classes of failures
- Experience communicating complex AI system designs and technical trade\-offs in writing and presentations to both technical and non\-technical audiences, including engineering leadership
### Preferred Qualifications:
- Experience applying AI and automation tooling to eliminate categories of engineering toil and measurably improve team\-level or organization\-level engineering efficiency
- Contributions to peer\-reviewed AI or systems research (e.g., NeurIPS, ICML, ICLR, MLSys, OSDI) or demonstrated track record of translating research advances into production AI systems
- Experience with large\-scale model training optimization, distributed training frameworks, or inference efficiency techniques such as quantization, distillation, or speculative decoding
- Experience defining and operationalizing privacy\-preserving or safety\-aware AI system designs in collaboration with policy, legal, or compliance stakeholders
### 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].
$219,000/year to $301,000/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 $219K-$301K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Meta, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($260K) sits 19% above the category median. Disclosed range: $219K to $301K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Meta AI Hiring
Meta has 40 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Research Scientist, AI Product Manager. Positions span Seattle, WA, US, Menlo Park, CA, US, New York, NY, US. Compensation range: $181K - $403K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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