Staff Forward Deployed Engineer

Role Description

We are looking for a Staff Forward Deployed Engineer who is passionate about solving complex cloud infrastructure challenges in the fast-growing AI/ML space. This is a high-impact position designed to be the "technical tip of the spear" for our most strategic AI-Native (ANE) customers.

As an FDE, you will sit at the intersection of Product Engineering and Customer Implementation. You will be embedded directly with our most strategic customers to drive transformational AI adoption. You will collaborate closely with customer teams to ship advanced AI applications that solve real world business problems.

You will build the tools, migration scripts, and AI starter kits that don't just solve one customer's problem, but scale the entire DigitalOcean AI Cloud ecosystem. Your mission is to accelerate "time-to-inference" in production at scale and serve as a critical feedback loop, ensuring our product roadmap is informed by the world's most demanding AI workloads.

What you’ll do:

• Embedded Customer Engineering: Partner deeply with strategic ANEs to manage complex migrations, build production-ready PoCs, and execute hands-on application builds on our GPU infrastructure.
• Build Scalable Assets & Tooling: Operationalize field learning by building migration planners, reusable systems, benchmarking frameworks, model optimization agents, and deployment automation scripts (e.g., Terraform/Pulumi) that standardize how AI workloads are deployed and tuned across DigitalOcean.
• Develop AI "Starter Kits": Create pre-built GenAI agents, solution templates, and notebooks (e.g., LangGraph, CrewAI) tailored to popular and advanced business needs.
• Drive the Product Roadmap: Act as the frontline technical voice for CPTO, surfacing architectural gaps and edge cases to inform core product development. Transition field-built tools into native product features.
• New Tech Validation: Lead the early testing and integration of emerging AI frameworks to ensure a first-class developer experience on DigitalOcean.
• Partner & GTM Enablement: Co-develop delivery frameworks with Strategic and Technical partners to enable repeatable, high-quality deployments at scale.
• Market Leadership: Produce field-tested demo kits and deployment guides that support new product releases with practical, validated assets.

Key Metrics

• Customer Adoption: Number of high-impact production workloads landed; reduction in average "time-to-production"; and Pilot-to-Production conversion rates.
• Asset & Tooling Delivery: Number of reusable tools (agents, scripts, playbooks) delivered per quarter and their subsequent adoption rate by the crossfunctional teams and customers.
• Field Enablement & Scale: Measured by the successful handoff and execution of FDE-defined deployment patterns to technical partners and the internal adoption rate of FDE playbooks across all customer-facing technical teams.
• Product Influence: Number of roadmap features or architectural changes directly attributed to FDE feedback and validated customer hypotheses.

Qualifications

• AI/ML Architecture & Domain Expertise: Significant experience in the AI/ML lifecycle, specifically hosting large language or multimodal models using inference engines like vLLM, SGLang, or Modular. Deep understanding of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).
• Distributed Systems & Infrastructure Mastery: Subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton). Expert proficiency in Kubernetes (K8s) and the design of distributed systems, including microservices, messaging systems, databases, and Infrastructure as Code.
• High-Performance Inference & Hardware Optimization: Hands-on experience with distributed inference serving frameworks (e.g., llm-d, NVIDIA Dynamo, or Ray Serve). Understanding of GPU-level optimization and experience with interconnect technologies like NVlink, XGMI, or RoCE.
• Software Engineering Foundation: Strong production coding skills in Python or Go with the ability to build high-quality tools, automation, and internal assets.
• Data-Driven Performance Insight: Proven ability to benchmark AI infrastructure and perform GPU utilization tuning to optimize customer ROI and workload performance.
• Consultative Execution: Proven ability to establish instant technical credibility with CTOs and Lead Architects while managing high-stakes technical migrations.

Requirements

• Experience with emerging AI orchestration frameworks like LangGraph, CrewAI, or LlamaIndex.
• A background in technical consulting or "Forward Deployed" roles at high-growth AI or infrastructure companies.
• Experience building and scaling "Center of Excellence" models or partner delivery frameworks.
• Active contributor to open-source AI projects or technical communities.

Compensation Range

$195,000 - $239,000

Benefits

• Competitive array of benefits to support well-being, including Employee Assistance Program and flexible time off policy.
• Reimbursement for relevant conferences, training, and education.
• Access to LinkedIn Learning's 10,000+ courses for continued growth and development.
• Bonus eligibility based on company and individual performance.
• Equity compensation for eligible employees, including equity grants upon hire and participation in Employee Stock Purchase Program.