Forward Deployed Engineer

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here.  We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. 

Position Overview

We are looking for a Senior Forward Deployed Engineer I (FDE) who is passionate about operationalizing and collaborating closely with strategic AI enterprises and high-growth startups to architect, implement, and fine-tune production infrastructure across DigitalOcean’s AI-Native Cloud.

As an AI Infrastructure Engineer within the Forward Deployed Engineering team, you sit at the intersection of deep systems engineering and high-impact customer architecture. You won't just build infrastructure in a vacuum; you will embed directly with customer engineering teams to solve complex infrastructure bottlenecks, optimize heterogeneous GPU cluster performance, and engineer mission-critical inference and training platforms.

If you thrive on squeezing maximum compute and memory throughput out of modern GPU clusters—whether optimizing NVIDIA (H100 /H200/B200/B300) or leveraging high-capacity AMD Instinct (MI300X/MI325X) hardware—and debugging low-level distributed stacks from drivers to orchestration, this is your playground.

Your mission is to accelerate production adoption of AI-native systems while helping shape the future of DigitalOcean’s AI-Native Cloud for the inference and agentic era.

What You’ll Do

Embed & Execute: Act as the primary technical authority on heterogeneous AI infrastructure for high-value DigitalOcean customers, co-engineering custom GPU infrastructure solutions for their production workloads.Optimize Multi-Vendor AI Pipelines: Architect and fine-tune low-latency, high-throughput LLM serving platforms across NVIDIA CUDA Or AMD ROCm™ platforms using serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang, TGI) and model execution techniques (quantization, KV caching, speculative decoding).Cluster Orchestration & SRE: Deploy, scale, and manage resilient Kubernetes clusters (DOKS/Bare Metal) tailored for compute-heavy AI workloads, utilizing tools like Ray, Slurm, and KubeFlow.Infrastructure as Code: Build scalable, repeatable blueprints using Terraform, Ansible, and Helm to automate multi-vendor GPU provisioning, high-speed networking, and storage stacks for customer deployments.Low-Level Heterogeneous Troubleshooting: Debug complex stack issues spanning host drivers (NVIDIA CUDA / AMD ROCm, HIP), container runtimes, inter-GPU communication libraries (NCCL / RCCL), high-speed interconnects (InfiniBand / RoCE / Infinity Fabric™), and distributed storage systems.Build for Scale: Translate common customer infrastructure challenges into core platform features, working directly with DigitalOcean’s product and core infrastructure teams to refine our cloud offering.Travel & Collaboration Requirements: Ability to travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration. Ability to consistently overlap with North American business hours, including availability until at least noon Eastern Time, to collaborate effectively with customers, Product, Engineering, and go-to-market teams.