Anyscale
Forward Deployed Engineer
About this role
Anyscale seeks a Forward Deployed Engineer to work directly with strategic customers across Latin America and beyond, helping them successfully adopt Ray and the Anyscale platform. You'll serve as a trusted technical advisor embedded within customer teams, translating business needs into solutions while gathering insights to shape product strategy.
What you'll do
- Lead onsite proof-of-value engagements and enterprise deployments with key customers
- Translate business objectives into technical solutions demonstrating clear ROI
- Build custom demos, reference architectures, and enablement programs for customer needs
- Advise across all organizational levels to drive confidence in Anyscale and Ray
- Collaborate with sales, product, and engineering teams to accelerate deals and resolve challenges
- Provide structured customer feedback to inform product roadmap and go-to-market strategy
What they're looking for
- Spanish and English fluency (written and spoken)
- Ray framework expertise and real-world application knowledge
- Enterprise SaaS and ML/AI solution adoption experience
- Solutions architecture and forward deployed engineering
- Executive and technical stakeholder engagement
- Complex infrastructure problem-solving
- Demo and reference architecture development
- Customer-focused business acumen
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Anyscale
Anyscale builds Ray, an open-source distributed computing framework and enterprise platform for scaling AI workloads across Kubernetes and cloud providers. The company is hiring forward-deployed engineers to work embedded with customers, software engineers to develop Ray Core, LLM inference specialists, and customer support engineers who combine technical expertise with post-sale success.
- Website
- anyscale.com
Likely interview questions
- Tell us about a time you worked embedded with a customer on a complex technical challenge. How did you translate their business objectives into a technical solution, and what was the outcome?
- Describe your experience with Ray or distributed computing frameworks. What types of ML workloads have you optimized, and how did you approach performance tuning?