Note: The job is a remote job and is open to candidates in USA. DigitalOcean is a cutting-edge technology company focused on simplifying cloud and AI for builders. They are seeking a Staff Forward Deployed Engineer to operationalize production AI-native workloads at scale, partnering with strategic customers to deploy and optimize AI systems.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration
Skills
- 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
- 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
- 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 with experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms
- Proven ability to benchmark and optimize AI infrastructure with strong focus on scalability, reliability, GPU efficiency, runtime performance, latency optimization, 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
- Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration
- Experience working in Forward Deployed Engineering, AI Infrastructure, Technical Consulting, AI Platform Engineering, 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 contributor 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
Benefits
- We provide employees with reimbursement for relevant conferences, training, and education.
- All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
- Employee Assistance Program
- Local Employee Meetups
- Flexible time off policy
- You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance.
- Equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
Company Overview
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