We are looking for a Senior Forward Deployed Engineer II (FDE) who is passionate about operationalizing production AI-native and agentic workloads at scale. This is a high-impact role designed to serve as the “technical tip of the spear” for DigitalOcean’s most strategic AI-native customers and platform initiatives.

As an FDE, you will operate at the intersection of Product Engineering, AI Infrastructure, and Customer Implementation. You will partner deeply with strategic AI-native enterprises (ANEs), startups, infrastructure vendors, and internal engineering teams to deploy, optimize, and scale production AI systems on DigitalOcean’s AI-Native Cloud.

This role extends beyond traditional GPU infrastructure deployment. You will work across Inference Engine, runtime systems, orchestration frameworks, and AI-native applications to help customers operationalize production AI and agentic systems with strong focus on scalability, reliability, latency, and workload economics.

FDE engineers also act as the “first customer” for new AI-native platform capabilities. You will validate products under real-world workloads, surface operational insights and architectural gaps, and help accelerate product maturity through continuous feedback loops with Product Engineering and Research teams.

You will build scalable deployment frameworks, benchmarking systems, automation tooling, and AI starter kits that transform field learning into reusable platform intelligence and repeatable deployment patterns across the DigitalOcean ecosystem.

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:

• Strategic AI Workload Operationalization: Partner with strategic ANEs and AI startups to architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud. Support complex migrations, production-ready PoCs, deployment acceleration, and long-term workload expansion across inference and runtime platforms.
• AI Performance & Systems Engineering: Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency/cost optimization.
• Platform Validation & Product Acceleration: Act as the “first customer” for DigitalOcean’s AI-native platform capabilities including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows. Surface real-world operational insights, architectural gaps, and scaling bottlenecks directly to Product Engineering and Research teams.
• Platform Intelligence & Automation: Build scalable deployment assets including benchmarking systems, automation tooling, AI starter kits, deployment frameworks, operational playbooks, finetuning workflows, and reference architectures that improve deployment velocity and platform adoption.
• Ecosystem & Technical Enablement: Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and launch readiness. Enable customer-facing technical teams and partner teams through validated deployment patterns, benchmarking insights, operational playbooks, reference architectures, demos, and technical guidance that help scale adoption of DigitalOcean’s AI-native platform.
• Travel: Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration.
  

Key Metrics

• Customer Adoption & Production Success: Measured by high-impact production workloads launched, reduction in time-to-production, pilot-to-production conversion rates, and expansion of AI-native platform adoption across strategic customers.
• Platform Intelligence & Product Influence: Measured by product improvements, roadmap influence, validated customer hypotheses, and operational insights generated from real-world production deployments.
• Asset & Tooling Delivery: Measured through adoption of FDE-built frameworks, automation tooling, benchmarking systems, operational playbooks, and reference architectures across customers and internal teams.
• Field Enablement & Ecosystem Scale: Measured through successful enablement of customer-facing teams, ecosystem collaboration outcomes, and adoption of FDE deployment standards across the AI-native ecosystem.

What You’ll Add to DigitalOcean

AI-Native Systems & Architecture Expertise: Experience designing and operationalizing production AI systems including inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications. Strong hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or equivalent systems, along with LLM optimization techniques including continuous batching, quantization, KV-cache optimization, and speculative decoding.Distributed Systems & Infrastructure Mastery: Deep expertise with NVIDIA and AMD GPU platforms and their software ecosystems including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE. Strong proficiency with Kubernetes (K8s), distributed systems, networking, storage systems, Infrastructure as Code, and large-scale AI infrastructure architectures.Runtime & Orchestration Systems: Experience with AI orchestration and agent frameworks such as LangGraph, CrewAI, MCP ecosystems, LlamaIndex, OpenAI Agents SDK, or similar runtime systems. Understanding of workflow orchestration, deployment systems, memory patterns, and AI-native application architectures.Software Engineering & Automation: Strong production coding skills in Python or Go with experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.Performance & Operational Intelligence: Proven ability to benchmark and optimize AI infrastructure with strong focus on scalability, reliability, GPU efficiency, runtime performance, latency optimization, and workload economics.Consultative & Cross-Functional Execution: Ability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners while managing high-impact production deployments and strategic technical initiatives.

Preferred Qualifications

• AI Infrastructure & Forward Deployed Engineering Experience: 6+ years of experience working in Forward Deployed Engineering, ML Engineer, Applied AI Engineer, AI Infrastructure, Technical Consulting, or equivalent customer-facing engineering roles supporting production AI systems.
• Platform Enablement & Ecosystem Experience: Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies across customer-facing and engineering organizations.
• Open Source & AI Ecosystem Involvement: Active contributor to open-source AI, infrastructure, orchestration, or developer tooling ecosystems.
• Vendor & Strategic Partnership Collaboration: Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness initiatives.
  

Compensation Range: 

• $167,000 - $209,000

*This is a remote role

JR: 2026-8207

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