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Meta is seeking a Software Engineering Director to lead the organization building the future of incident response. As agents increasingly build and operate infrastructure, Meta needs reliability systems that can reason and act at machine speed. This leader will drive Meta's AI-powered incident response agent, Meta’s AI agent for incident response, toward a human-on-the-loop model: the agent autonomously gathers context, investigates incidents, identifies root causes, creates a safe mitigation plan, and executes the appropriate action, while humans set boundaries, supervise consequential actions, and intervene when judgment is required.This role combines frontier agent capabilities with one of the world’s largest and most complex production environments. You will define the technical, product, and organizational strategy, lead an engineering organization with real production systems, and partner across infrastructure and product groups to make incident response faster, safer, and dramatically less labor-intensive. Few roles offer the opportunity to define both the technology and operating model for autonomous incident mitigation at this scale.
Software Engineering Director, AI for Incident Response Responsibilities:
- Define and drive the engineering strategy and roadmap for AI-powered incident response, progressing from investigation and diagnosis to safe, end-to-end mitigation and continuous learning
- Lead the architecture and delivery of an agent that reasons across telemetry, code, configurations, deployments, service dependencies, and incident history to identify root causes and take appropriate action
- Establish a human-on-the-loop operating model with progressive autonomy, explicit policy and permission boundaries, independent validation, auditability, rollback, and clear escalation paths
- Build Opsmate as an extensible platform that combines shared, out-of-the-box capabilities with domain-specific skills and agents contributed by product groups and individual on-call engineers
- Establish trusted evaluation and measurement systems that connect agent quality to customer and reliability outcomes, including investigation accuracy, adoption, time to mitigation, and reduced operational effort
- Partner deeply with product groups, infrastructure teams, product management, and data science to understand what success means for their services and build toward shared outcomes
- Ensure the platform itself meets a high bar for reliability, latency, scale, security, privacy, and cost in Meta’s production environment
- Build and grow an engineering organization that consistently delivers measurable results by attracting, developing, and retaining engineers and engineering managers across distributed systems, reliability, and applied AI
- Create a culture of technical depth, rapid learning, operational excellence, and accountability for realized customer impact
- Contribute to company-wide engineering and AI initiatives, including recruiting, technical standards, and responsible AI practices
Minimum Qualifications:
- 10+ years of software engineering experience, including experience in technical leadership roles
- 7+ years of engineering management experience, including leading multiple teams or managers and delivering products or systems with measurable impact
- Experience defining and executing engineering strategy for large-scale distributed systems, reliability platforms, developer infrastructure, or production AI systems
- Experience building and operating systems that support critical, always-on production workloads
- Track record of building broadly adopted platforms and driving outcomes across organizational boundaries
- Experience evaluating complex automated systems and establishing safeguards for trustworthy production operation
- Experience building engineering teams through hiring, onboarding, and developing engineers and managers at multiple levels
- Experience partnering cross-functionally with product, data science, and infrastructure organizations
Preferred Qualifications:
- Experience with incident management, SRE, observability, oncall operations, or automated remediation at significant scale
- Experience designing safe execution systems, including identity, authorization, sandboxing, validation, rollback, and auditability
- Experience building extensible platforms that allow domain teams to contribute specialized capabilities while preserving a coherent user experience and control plane
- Track record of taking a technically ambitious product from early adoption to trusted, broad production use
- Experience managing other engineering managers and building a deep technical and management bench across multiple levels
- Experience operating LLM-based or tool-using agents in production, including evaluation, orchestration, and progressive autonomy
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].
$271,000/year to $347,000/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 $271K-$347K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $193K across 172 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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% 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 $220,000 based on 1,448 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($309K) sits 40% above the category median. Disclosed range: $271K to $347K.
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 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 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).
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 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
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