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DigitalOcean

Staff Forward Deployed Engineer, AI/ML

Salary$220k – $239k
LocationBay Area Metro, Seattle
Work modehybrid
Typefull-time
DepartmentIaaS & Data Centers
EquityIncluded
Company size1,001–5,000 people
First seen4mo ago
Last seen1d ago
About the company
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.
About the role
We are looking for a Staff Forward Deployed Engineer (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
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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.
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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.
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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.
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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.
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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.
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Travel: Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration.
Key Metrics
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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.
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Platform Intelligence & Product Influence: Measured by product improvements, roadmap influence, validated customer hypotheses, and operational insights generated from real-world production deployments.
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Asset & Tooling Delivery: Measured through adoption of FDE-built frameworks, automation tooling, benchmarking systems, operational playbooks, and reference architectures across customers and internal teams.
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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
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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.
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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.
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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.
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Software Engineering & Automation: Strong production coding skills in Python or Go with experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.
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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.
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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
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AI Infrastructure & Forward Deployed Engineering Experience: Experience working in Forward Deployed Engineering, AI Infrastructure, Technical Consulting, AI Platform Engineering, or equivalent customer-facing engineering roles supporting production AI systems.
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Platform Enablement & Ecosystem Experience: Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies across customer-facing and engineering organizations.
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Open Source & AI Ecosystem Involvement: Active contributor to open-source AI, infrastructure, orchestration, or developer tooling ecosystems.
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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:
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$220,000 - $239,000
*This is a hybrid role
JR: 2026-7748
#LI-Hybrid
Why You’ll Like Working for DigitalOcean
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We innovate with purpose. You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
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We prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. 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.
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We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
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We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
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DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Legal
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.
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