We are looking for an engineer to play a key role in providing technical, engineering support to Meta's Advertising partners, Customers and clients globally. You will have the opportunity to work together with a global team of Business Support Engineers who are expert in Meta's AdTech to provide proactive and reactive support for partner issues and integrations while partnering with our broad cross functional teams to ensure a high quality for our products and satisfactory experience for our partners and customers. We work directly with Platform and Infrastructure teams to investigate and resolve issues reported by our partners, to properly assess and agree on actions to be taken for necessary fixes and continuous improvements in our products and deployments. As a Business Support Engineer working on Ads at Meta, you will primarily support on Meta's Ads Products but also have the opportunity to work cross functionally in several of our key business areas across Business Messaging, Telecommunications and combine your experience with customer service skills with a product focus to ensure that key insights are communicated to our product teams. We are looking for people who have subject-matter knowledge in managing and maintaining integrations with third-party services, who help foster developer/business relationships, who demonstrate critical thinking and blending systems design with business needs, and who have a desire to improve the support experience of our customers. As a Business Support Engineer, you will understand industry trends, partner solutions and integrations, and their implications to our product roadmap. You will work closely with other regional offices and partnership teams to support a broad range of partners across the globe to integrate Meta's Business Products into their offering.
Responsibilities
Provide proactive and reactive engineering support for partners, independently managing complex outages to ensure high partner satisfaction
• Troubleshoot large-scale distributed systems and partner integrations, maintaining high code quality and operational standards
• Leverage AI tools to accelerate troubleshooting, automate repetitive tasks, and scale your impact with an 'AI native' mindset
• Leverage AI tools to accelerate troubleshooting, automate repetitive tasks, and scale your impact with an 'AI native' mindset
• Develop performance monitoring systems for partner integrations to ensure high availability; leverage metrics to proactively identify issues and drive improvements across teams
• Provide 24/7 oncall support coverage via rotation schedule (including weekends)
• Collaborate with Platform and Infrastructure teams to investigate issues, align on fixes, and drive continuous product improvement
• Create clear documentation, specs, guides, and presentations to communicate complex AI concepts to diverse audiences, scaling the team's knowledge internally and externally
• Drive end-to-end execution, using sound judgment to manage stakeholder expectations and ensuring clear alignment. Develop and share AI/ML expertise, actively coach and mentor other engineers on technical troubleshooting and project execution
• Proactively and independently analyze information to identify specific trends/opportunities, recommend appropriate and relevant tactical improvements, anticipate future business needs to improve the overall support experience, and share this knowledge across the team
Minimum Qualifications
• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• 3+ years of experience as a software engineer or site reliability engineer
• Proven experience as a support engineer, service engineer, or similar role
• Bachelor's degree in Computer Science, Engineering, or related technical field experience
• Experience in developing, deploying, and operating software in one or more public cloud infrastructures (Azure, GCP, AWS, etc.)
• Proven experience in API development on cloud-based infrastructures
• Proficiency in full-stack development with experience in the full web stack, SOAP, and REST API technologies and architectures
• Experience in communicating with technical and business audiences, and creating technical documentation
• Experience working with IT infrastructures and network protocols across multiple layers, including familiarity with data exchange formats and protocols
• Experience in assessing, analyzing, and resolving operational issues using data
• Experience managing multiple concurrent projects and driving initiatives in a cross-functional environment
Preferred Qualifications
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience working in engineering environments with geographically distributed, cross-cultural teams and international stakeholders
• Experience in partner-facing or customer-centric engineering roles
• Experience building and deploying solutions on cloud platforms (e.g., AWS, GCP, Azure)
• Hands-on experience working with large language models and AI agents
• Experience in adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience transforming data, model selection, training, and optimization, and deployment at scale
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with Open Source cloud stacks like Kubernetes, Kubeflow, Docker containers
