Business Support Engineer - Meta Business Agent

The Meta Game, Inc.
Menlo Park, CA, United States
6 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$142,000.0 - $201,000.0
Working hours
Regular working hours

Tech stack

JavaScript (Programming Language) PHP (Programming Language) Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Data Analysis Microsoft Azure Software Bug Management Cloud Computing Data Transformation Software Debugging Distributed Systems
+18 more
Python (Programming Language) Open Source Technology Reliability Engineering Software Engineering SQL Databases Google Cloud Pytorch ReactJS System Availability Large Language Models Multi-Agent Systems Model Validation Kubernetes Performance Monitor Api Design Restful APIs GPT Docker

Job description

Meta recently launched its Business Agent, helping businesses of every size use AI to boost productivity and deliver more personalized customer experiences. Business Support Engineering will be at the forefront of this shift, and we’re looking for an engineer to play a pivotal role supporting Meta’s partners bringing demonstrated experience in distributed systems and API troubleshooting and a focus on improving the end-to-end support experience.As a Business Support Engineer, you will work closely with cross-functional teams and business partners across the globe, incorporating AI-driven business solutions into their service offerings. You will track industry advancements and partner experiences, evaluating their impact and influencing the product’s strategic roadmap., 1. Provide proactive and reactive engineering support for partners, independently managing complex outages to ensure high partner satisfaction

  1. Troubleshoot large-scale distributed systems and partner integrations, championing operational excellence and engineering craftsmanship
  2. Leverage AI tools to accelerate troubleshooting, automate repetitive tasks, and scale your impact with an ‘AI native’ mindset
  3. Build, launch, and optimize AI solutions using Llama and other LLMs, owning the full lifecycle from prototype to production
  4. Develop performance monitoring systems for partner integrations to ensure high availability
  5. leverage metrics to proactively identify issues and drive improvements across teams
  6. Provide 24/7 oncall support coverage via rotation schedule (including weekends)
  7. Collaborate with Platform and Infrastructure teams to investigate issues, align on fixes, and drive continuous product improvement
  8. Create clear documentation, specs, guides, and presentations to communicate complex AI concepts to diverse audiences, scaling the team’s knowledge internally and externally
  9. 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 peers on technical troubleshooting and project execution

Requirements

  1. 5+ years of experience in Software Engineering or Site Reliability Engineering
  2. Proven experience in API development on cloud-based infrastructures, with the ability to debug, identify root causes, and independently resolve outages impacting Meta partners
  3. Experience with the full web stack, REST APIs, Python, PHP/Hack, and JavaScript/React development, along with debugging and bug management
  4. Knowledge on fine-tuning and optimizations of PyTorch models and with at least one LLM such as LLaMA, GPT, Claude, Falcon, etc
  5. Experience in communicating with technical and business audiences and writing technical documentation
  6. Experience in assessing, analyzing, and resolving operational issues using data analysis (SQL), 1. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  7. Experience working in engineering environments with geographically distributed, cross-cultural teams and international stakeholders
  8. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  9. Experience in partner-facing or customer-centric engineering roles
  10. Hands-on experience working with large language models and AI agents
  11. Experience transforming data, model selection/training/optimization, and deployment at scale
  12. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  13. Experience with Open Source cloud stacks like Kubernetes, Kubeflow, Docker containers
  14. Experience building and deploying solutions on cloud platforms (e.g., AWS, GCP, Azure)

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:24 min

Building client-facing AI agents for engineering teams

Alfonso Graziano Alfonso Graziano · Coffee With Developers

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Generative pre-trained transformer models powering code completions

lgonta lgonta +1 · World Congress 2024

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Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

1:21 min

Exploring the target application for front end tests

Anna Mcdougall · JS Congress

2:02 min

Moving from playing with artificial intelligence to business implementation

Lee Stott · Coffee With Developers

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Assessing GPT-4o performance for pull request feedback

Merrill Lutsky Merrill Lutsky · World Congress 2025

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