Senior Forward Deployed Engineer II – AI/ML

🔥 1 minute ago

⛰️ Colorado – Remote

⛰️ Colorado – Remote

💵 $167k - $209k / year

⏰ Full Time

🟠 Senior

🤖 Artificial Intelligence

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DigitalOcean

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2011

☁️ SaaS

SaaS • Cloud Computing

DigitalOcean is a cloud infrastructure provider that offers a suite of products and services for developers to build, deploy, and scale applications. Their platform provides comprehensive tutorials, reference material, and support documentation to assist users in managing resources effectively using their API and CLI tools. With features like Droplets (virtual machines), managed databases, Kubernetes, and a marketplace for third-party applications, DigitalOcean focuses on simplicity and performance. They cater to both individual developers and larger organizations looking for cloud solutions that are easy to implement and manage.

📋 Description

• Partner with strategic AI-native enterprises 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 proofs of concept, 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 AI-native platform capabilities, including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows • Surface operational insights, architectural gaps, and scaling bottlenecks to 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 technical teams and partner teams through 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 • Deliver outcomes measured by production workload launches, reduced time-to-production, pilot-to-production conversion, platform adoption, product improvements, roadmap influence, tooling adoption, and ecosystem enablement

🎯 Requirements

• Experience designing and operationalizing production AI systems, including inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications • Hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or equivalent • Experience with LLM optimization techniques including continuous batching, quantization, KV-cache optimization, and speculative decoding • Deep expertise with NVIDIA and AMD GPU platforms and 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 • Strong production coding skills in Python or Go • 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 optimization, and workload economics • Ability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners • 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 • 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

🏖️ Benefits

• Reimbursement for relevant conferences, training, and education • Access to LinkedIn Learning's 10,000+ courses • Employee Assistance Program • Local Employee Meetups • Flexible time off policy • Bonus eligibility based on company and individual performance • Equity compensation, including equity grants upon hire • Option to participate in the Employee Stock Purchase Program • Competitive benefits varying based on local regulations and preferences