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Open nowFirst seen 2 hours ago

Account Executive, Model AI API (Singapore)

Meta1,308 open roles

Where
Singapore
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Your applicationOpen nowAccount Executive, Model AI API (Singapore)Meta · Singapore

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The clock on this job

Early applications get read.

12.9% of postings close within 7 days. Measured by our own scanner across the market. Meta postings stay open a median of 35 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 12.9%7 days
  4. 21.0%14 days
  5. 35.6%30 days
  6. 58.5%60 days
This job: first seen 2 hours ago

Meta median: 35 days open

The posting

Meta is seeking an Account Executive to help businesses build and scale AI-powered products with the Muse Spark AI API. You will work with AI-native companies, software providers, and enterprise customers to identify high-value use cases, guide model evaluations, and turn successful trials into paying customers and sustained API consumption.

This role combines enterprise sales expertise, technical fluency, and a builder mindset. You will own customer relationships from prospecting through commercial conversion and expansion, partnering with Solutions Architects, Partner Engineering, Product, Marketing, Engineering, Operations and other cross-functional teams to help customers move from evaluation to production. You will also bring customer insights back to Meta and help build a repeatable sales motion for a rapidly evolving AI market. We are looking for someone who can earn the trust of technical and business leaders, translate model capabilities into measurable business value, and deliver results in an environment where the playbook is still being built.

Responsibilities

  • Develop and execute territory and account plans to acquire new customers and expand existing relationships. Prioritize opportunities based on customer needs, model fit, production readiness, and potential for paid API consumption
  • Own the sales cycle from targeted outbound and inbound qualification through discovery, evaluation, commercial conversion, and expansion. Deliver against revenue and consumption goals, with a focus on customers putting AI into production
  • Build relationships with founders, CTOs, AI and engineering leaders, product executives, and business decision-makers. Develop technical champions and executive sponsors, and align the stakeholders involved in a purchase
  • Lead consultative discovery to understand customers' AI applications, model stack, workloads, buying process, and success criteria. Develop business cases that connect model quality, latency, throughput, and total cost of ownership to customer outcomes
  • Partner with Solutions Architects and Partner Engineering to scope evaluations and proof-of-concept engagements. Establish joint action plans with measurable success criteria, milestones, owners, and a clear path to paid production usage
  • Guide customers through approved commercial and onboarding paths. Coordinate with technical, operations, finance, and legal partners to address integration, billing, procurement, security, and data-handling questions, and escalate blockers to the appropriate owners
  • Grow consumption by identifying additional workloads, applications, and teams within customer accounts. Partner with technical and customer-facing teams to support adoption and recognize usage trends that create expansion opportunities or signal risk
  • Maintain accurate CRM records, pipeline reporting, and revenue and consumption forecasts. Quantify opportunity potential using customer workload assumptions, expected adoption, usage patterns, and commercial milestones
  • Collaborate with Business Development and channel teams where relevant to customer needs and supported routes to market. Coordinate account ownership and handoffs, and engage the appropriate partners for strategic or distribution opportunities
  • Capture customer feedback, evaluation outcomes, competitive insights, and adoption barriers to inform product priorities and go-to-market decisions. Turn customer learnings into reusable discovery guides, value propositions, evaluation playbooks, and enablement resources; use AI tools and automation to improve sales productivity

Minimum Qualifications

  • 8+ years of B2B technology sales experience, including selling enterprise software, cloud platforms, developer tools, or API-based products
  • Experience owning a sales target and meeting or exceeding revenue or consumption goals through new customer acquisition and account expansion
  • Experience managing sales cycles and complex agreements involving technical evaluation and multiple stakeholders, including executive sponsors, engineering teams, and procurement
  • Experience translating technical capabilities into business value and presenting to both technical and non-technical audiences
  • Experience partnering with Solutions Architects, Sales Engineers, or equivalent technical teams to advance customer evaluations and adoption
  • Experience developing account plans, managing and owning pipeline and forecasts, and using data to prioritize opportunities and assess risk
  • Bachelor's degree or equivalent practical experience

Preferred Qualifications

  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Experience building sales playbooks for emerging products, collaborating across functions in ambiguous environments, and using AI tools to improve research, outreach, and account planning
  • Experience converting pilots into paid production deployments, expanding usage across workloads, and building customer-specific return-on-investment or total-cost-of-ownership analyses
  • Experience working with AI-native companies, independent software vendors, or enterprise teams integrating AI into customer-facing products or internal workflows
  • Experience selling generative AI, large language model APIs, AI infrastructure, or consumption-based cloud and developer platforms
  • 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)
  • Understanding of LLM application patterns and evaluation trade-offs, including agents, retrieval-augmented generation, coding workflows, summarization, model routing, and token-based economics
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