Senior Forward Deployed Engineer II (AI/ML)

DigitalOcean•Seattle, WA

1d•$167,000 - $209,000•Hybrid

About The Position

DigitalOcean is seeking a Senior Forward Deployed Engineer to operationalize production AI-native and agentic workloads at scale. The role serves as a technical lead for strategic AI-native customers and platform initiatives, working across Product Engineering, AI Infrastructure, and Customer Implementation. The engineer partners with enterprises, startups, infrastructure vendors, and internal teams to deploy, optimize, and scale production AI systems on DigitalOcean’s AI-Native Cloud. The position goes beyond GPU infrastructure deployment, spanning inference engines, runtime systems, orchestration frameworks, and AI-native applications. It focuses on scalability, reliability, latency, and workload economics. The engineer also acts as an early user of new platform capabilities, validating them under real-world workloads and providing operational feedback to Product Engineering and Research teams. They build reusable deployment frameworks, benchmarking systems, automation tools, AI starter kits, and reference materials to turn field experience into repeatable platform practices. The role involves travel of up to 30% for customer engagements, onsite workshops, ecosystem partnerships, conferences, and internal collaboration. It is hybrid. The listed compensation range is $167,000–$209,000, with a potential performance-based bonus and equity compensation for eligible employees.

Requirements

Hands-on 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.
Experience with LLM optimization techniques such as continuous batching, quantization, KV-cache optimization, and speculative decoding.
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, distributed systems, networking, storage systems, Infrastructure as Code, and large-scale AI infrastructure architectures.
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.
Strong production coding skills in Python or Go, including experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.
Proven ability to benchmark and optimize AI infrastructure for scalability, reliability, GPU efficiency, runtime performance, latency, and workload economics.
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.

Nice To Haves

6+ years of experience in Forward Deployed Engineering, ML Engineering, Applied AI Engineering, AI Infrastructure, Technical Consulting, or equivalent customer-facing engineering roles supporting production AI systems.
Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies across customer-facing and engineering organizations.
Active contribution to open-source AI, infrastructure, orchestration, or developer tooling ecosystems.
Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness initiatives.

Responsibilities

Architect, deploy, optimize, and scale production AI and agentic systems for strategic AI-native enterprises and startups on DigitalOcean’s AI-Native Cloud.
Support complex migrations, production-ready proofs of concept, deployment acceleration, and workload expansion across inference and runtime platforms.
Benchmark and optimize distributed inference and runtime performance, including GPU efficiency, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency and cost.
Validate AI-native platform capabilities—including inference engines, runtimes, orchestration systems, GPU platforms, and deployment workflows—under real-world workloads.
Share operational insights, architectural gaps, and scaling bottlenecks with Product Engineering and Research teams.
Build benchmarking systems, automation tooling, AI starter kits, deployment frameworks, operational playbooks, finetuning workflows, and reference architectures.
Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and launch readiness.
Enable customer-facing and partner teams with validated deployment patterns, benchmarking insights, operational playbooks, reference architectures, demos, and technical guidance.
Travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration.