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5 часов назад

Forward Deployed Engineer (AI)

250 000 - 275 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Forward Deployed Engineer (AI): Delivering Ray and Anyscale platform solutions for strategic customers, including proof-of-value engagements, deployments, and enterprise adoption with an accent on distributed machine learning, Kubernetes, and customer-specific architectures. Focus on translating business objectives into measurable ROI, optimizing training and inference workloads, and feeding customer insights into product and engineering direction.

Location: San Francisco, United States; requires onsite work with key customers and frequent travel.

Salary: $250,000–$275,000 per year, plus equity and benefits.

Company

hirify.global commercializes Ray, an open-source distributed computing platform for scaling machine learning applications from laptops to clusters.

What you will do

  • Partner with strategic customers to lead proof-of-value engagements, deployments, and enterprise adoption of Ray and the hirify.global platform.
  • Translate business objectives into technical solutions that demonstrate measurable ROI and strategic impact.
  • Build and deliver customer-specific demos, reference architectures, and enablement programs.
  • Advise technical and executive stakeholders and build confidence in hirify.global and Ray.
  • Collaborate with sales, product, and engineering to accelerate deals, resolve challenges, and drive long-term customer success.
  • Provide structured customer feedback to influence product direction and go-to-market strategy.

Requirements

  • Fluency in Spanish and English is required for working with Spanish-speaking technical and executive stakeholders.
  • 5+ years of customer-facing experience in forward deployed engineering, solutions architecture, field engineering, or software engineering.
  • Strong technical foundation with Ray or the ability to quickly apply Ray to real-world use cases.
  • Hands-on experience with machine learning training and inference, including distributed training, model serving, and performance and cost tradeoffs.
  • Working knowledge of Kubernetes and container orchestration in Kubernetes-based environments.
  • Experience driving enterprise adoption of complex SaaS, infrastructure, or ML/AI solutions and engaging both executive and technical stakeholders.

Nice to have

  • Experience working directly with Spanish-speaking customers across Latin America and other regions.

Culture & Benefits

  • Stock options and equity participation.
  • Healthcare premiums covered at 95%.
  • 401(k) retirement plan.
  • Wellness and education stipend.
  • Paid parental leave, fertility benefits, paid time off, and commute reimbursement.
  • Lunch provided when working in the office.

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